Arab J Geosci DOI 10.1007/s12517-014-1714-2
ORIGINAL PAPER
Improved feature extraction scheme for satellite images using NDVI and NDWI technique based on DWT and SVD A. K. Bhandari & A. Kumar & G. K. Singh
Received: 19 August 2014 / Accepted: 10 November 2014 # Saudi Society for Geosciences 2014
Abstract In this paper, an improved feature extraction technique using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) with discrete wavelet transform (DWT) and singular value decomposition (SVD) enhancement approach has been proposed. DWT-SVD is used for quality improvement of the lowcontrast satellite images. The NDVI and NDWI have been successfully used to delineate vegetation land cover and surface water features. The method employs multispectral remote sensing data technique to find spectral signature of different objects such as vegetation index and water body classification presented in the satellite image. The input image is decomposed into the four frequency subbands through DWT, and then obtains the singular value matrix of the low– low thresholded subband image, and finally, it reconstructs the enhanced image by applying inverse DWT. The basic enhancement occurs due to scaling of singular values of DWT coefficients. The simulation results clearly show the increased efficiency and flexibility of the proposed method over existing methods such as decorrelation stretching technique.
Keywords Image enhancement . SVD . DWT . NDVI . NDWI . Remote sensing images
A. K. Bhandari (*) : A. Kumar PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482005, MP, India e-mail:
[email protected] A. Kumar e-mail:
[email protected] G. K. Singh Department of Electrical Engineering, Indian Institute of Technology Roorkee, Uttarakhand 247667, India e-mail:
[email protected]
Introduction Contrast enhancement is nowadays referred as one of the most important issues in image processing. Image enhancement is the technique, which is most widely required in the field of image processing to improve visualization features presented in the image. It plays a crucial role which is used as a means of supplementing the information in various fields such as inspection, military, satellite image, medical image analysis, and several other data visualization applications (Demirel et al. 2010; Bhandari et al. 2011). Usually, an image holds several ill-defined and ambiguous areas. In case of satellite images, precisely finding efficient enhanced result in the existence of inherent uncertainty and ambiguity is a challenging task. Such suitable and accurate multispectral remote sensing image enhancement can appreciably support the applications in numerous fields ranging among agriculture, defense, geology, environmental science, etc. The gray value allocated to a pixel is also the typical reflectance of many kinds of land covers. Therefore, appointing proper boosted features with firmness is an inherent problem for satellite images. In general, raw satellite images have a relatively narrow range of brightness values. Therefore, contrast enhancement is frequently employed to enhance the multiband satellite images for better interpretation and visualization (Gonzalez et al. 2004). Most common problem that occurs in the satellite images while capturing image with a huge amount of distance is the dark light and contrast of an image (Ibrahim and Kong 2007). Basically, a contrast is developed due to difference in luminance, which is reflected from two surfaces. If an image has been taken in very dark or a very bright situation, the information may be lost in the areas, which are excessively and uniformly dark or bright. In this situation, it is very difficult to improve contrast of an image, which has complete information but not visible. Therefore, several techniques have been reported in literature for contrast analysis of the satellite image
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such as decorrelation stretching (Alley 1996), general histogram equalization (Shanmugavadivu et al. 2014) and discrete wavelet transform (DWT) (Zhiwen et al. 2010; Cho, and Bui 2014; Bhutada et al. 2011; Tello et al. 2011), discrete cosine transforms (DCT) (Mukherjee and Mitra 2008; Tang 2004), and discrete Fourier transform (DFT) (Agaian et al. 2001). Over the years, satellite images are used in many applications such as geographical information system, astronomy, and geoscience studies. Image enhancement is a decisive and fundamental step for remote sensing information retrieval and classification. It is typically used to locate objects and boundaries (lines, curves, etc.) in images. So, extracting the target information from the high-resolution remote sensing images has become a challenging problem (Bhandari et al. 2014a; Soni et al. 2013; Kumar et al. 2012; Bhandari et al. 2012a, b, c). As an advanced recognition technology, remote sensing has been widely useful in many areas, such as military reconnaissance, target surveillance, effect assessment of combating or damage, monitoring of crop development and assessing of grain yield, worldwide survey of surface resources, and several other areas (Bhandari et al. 2014b). Remote sensing has brought enormous economic and social benefits and shown an extensive application prospects. Remote sensing image processing is a means of application of remote sensing image, and remote sensing image is the purpose and end-result (Demirel and Anbarjafari 2011). As the remote sensing image signal will inevitably lose some details in access and communication, it is necessary to improve remote sensing image. The operations of remote sensing image enhancement consist of contrast enhancement, edge improvement, color enhancement, nonlinear strecth, linear enhancement, radiometric enhancement, spatial filtering, noise suppression, image smoothing, and image sharpening, and so on. Image enhancement technology which is based on DWT analysis for remote sensing image is a newly developed technique (Bhandari et al. 2012a, b, c). Several enhancement methods have been exploited to increase the image contrast and brightness. A wide variety of image enhancement approaches using different concepts have appeared in the literature (Bhandari et al. 2014c; Ashish et al. 2011; Bhandari et al. 2012a, b, c). In this paper, an improved technique based on DWT-singular value decomposition (SVD) has been proposed for enhancement and feature extraction of low-contrast satellite color images using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). The SVD technique is based on a theorem from linear algebra which says that a rectangular matrix A can be broken down into the product of three matrices, as follows: (i) an orthogonal matrix UA, (ii) a diagonal matrix ΣA, and (iii) the transpose of an orthogonal matrix VA. The singular value-based image equalization (SVE) technique is based on equalizing the singular value matrix
obtained by SVD. The SVD of an image can be interpreted as a matrix, and is written as A ¼ U A ∑A V TA
ð1Þ
where UA and VA are orthogonal square matrices known as hanger and aligner, respectively. The ΣA matrix contains the sorted singular values on its main diagonal, and basic enhancement occurs due to scaling of singular values of the DWT coefficients. The singular value matrix represents the intensity information of image, and any alteration on the singular values changes the intensity of the input image. In case of SVD, the ratio of the highest singular value of the generated normalized matrix, with mean zero and variance of one, over a particular image can be as max ∑N ðμ¼0;var¼1Þ ξ¼ ð2Þ max ∑A where ΣN(μ=0,var=1) is singular value matrix of the synthetic intensity matrix. These coefficients can be used to regenerate an equalized image using E equalized A ¼ U A ξ∑A V TA ð3Þ where Eequalized A is used to denote equalized image, named A. The equalization of an image is done to remove the problem of the illumination, which is basically one cause of the lowcontrast image and blurring. In this work, DWT is applied on color satellite image of each band such as near-infrared (NIR), red, and green band, to extract better texture features presented in the low-contrast image. DWT original image into four subband, as low–low (LL), low–high (LH), high–low (HL), and high–high (HH). The frequency components of these subband images cover all the frequency components of the original image. Hence, after inverse DWT (IDWT), the enhanced image will be more effective, sharper, and having a good contrast. Many researchers have reported the use of NDVI (Goward et al. 1991) for vegetation monitoring (Tucker 1979) assessing the crop cover (Ayyangar et al. 1980), drought monitoring (Peters et al. 2002; Singh et al. 2003), and agricultural drought assessment at national level (Ji and Peters 2003). In the NDVI and NDWI techniques, initially different bands (red, NIR, and green) are computed from the satellite image, and then, NDVI or NDWI methods are applied according to its characteristic. However, two major issues have been frequently encountered: (a) NDWIs calculated from different band combinations [visible, NIR] can produce diverse results, and (b) NDWI thresholds vary depending on the proportions of subpixel
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water/nonwater components. We have to assess all the NDWIs for selecting the best performing index and to create appropriate thresholds for clearly distinguishing water characteristics. We found that the NDWI calculated from (green–NIR)/(green + NIR), where NIR is the shorter wavelength region (0.76 to 0.90 μm), has the most stable threshold. Therefore, it is highly suggested that this NDWI be utilized for mapping water bodies, but adjustment of the threshold values focused around actual circumstances is vital (Ji et al. 2009). Therefore, a new technique for enhancement is proposed based on combined effect of DWT-SVD to extract more accurate versatile feature of satellite images using NDVI technique. The rest of the paper is organized as follows: Section “Overview of remote sensing, NDVI, NDWI, SVD, and GHE techniques” presents the overview of remote sensing, NDVI, NDWI, and SVD methods. Section “Proposed methodology” gives complete explanations of the proposed methodology. Section “Result and discussion” reports the visual, qualitative, and quantitative results of the employed method with some discussions supported by mean, variance as a fidelity parameters and NDVI-NDWI as a threshold values. Finally, conclusions are drawn in section “Conclusion.”
Overview of remote sensing, NDVI, NDWI, SVD, and GHE techniques The multispectral satellite images contain essential integrating spectral and spatial features of objects. Digital image processing of remote sensing images presents tools for analyzing the captured information through the numerous procedure and mathematical indices to classify the object and enhancement of the raw data. Remote sensing Remote sensing sensor systems detect reflected or emitted radiation from features on the Earth’s surface. Remote sensing is the electronic acquisition and digital analysis of Earth imagery system. It is science of deriving information about an object from measurements made at a distance from the object, i.e., without coming into contact with it. The main aim of remote sensing is to calculate the percentage of different features like vegetation area, river, and water bodies and to subsequently make them available to the public for further analysis in order to avoid any sort of natural disasters like flood. Therefore, several techniques have been developed for feature extraction in which NDVI (Goward et al. 1991; Van and Owe 1993) and NDWI (Gao 1996) are most widely used for satellite image processing.
NDVI The NDVI is a simple numerical indicator that can be used to analyze remote sensing measurements from a remote platform and assess whether the target or object being observed contains live green vegetation or not (Santos and Negri 1996). The spectral reflectance difference between NIR and red is used to calculate vegetation and other features by using NDVI technique. The NDVI is a vegetation index defined by bands 3 and 4 (visible red and NIR) (fundamentals of remote sensing). NDVI is the most common index utilized. NDVI from reflectance images is obtained by mean of channels 3 red band (0.63–0.69 μm) and 4 NIR band (0.78–0.90 μm) or with mean of channel 4 and channel 2 green band (0.52–0.60) given in Eq. (4), whereas NDVI is calculated from these individual measurements as follows: RNDVI ¼
N IR−Red ; ðwhere 0 < NDVI > 1Þ N IR þ Red
ð4Þ
N IR−Green GNDVI ¼ ; ðwhere 0 < NDVI > 1Þ N IR þ Green
where RED is visible red reflectance, and NIR is NIR reflectance. The range of NDVI can be written as −1 to 1. However, most commonly used NDVI range is 0 to 1. Since calculation of NDVI image for vegetation detection using the specified range, there is no use of negative threshold value. Therefore, in Eq. (4), the range of NDVI has been provided from 0 to 1. In addition, the specified standard range and corresponding characteristics of different band have been given in Table 1, which indicates the thematic bands of NASA as LANDSAT satellite. Value of NDVI should be larger for greater chlorophyll density (Hu et al. 2008). It takes the difference of NIR and red band and normalizes it to balance out the effects of uneven illumination such as shadows of clouds or hills. In other words, on pixel by pixel basis, subtracts the value of red band from value of NIR band and divides by their sum. Very low values (0.1 and below) of NDVI correspond to barren areas of rock, sand, or snow. Moderate values (0.2 to 0.3) represent shrub and grassland, while high values (0.6 to 0.8) indicate temperate and tropical rainforests. The range of NDVI values can be from −1.0 to 1.0, where higher values are for green vegetation and low values for other common surface materials (Karaburun 2010). Bare soil is represented with NDVI values which are closest to 0, and water bodies are represented with negative NDVI values. Remotely sensed vegetation indices and water indices such as NDVI and NDWI are most efficiently utilized which have countless advantage in the assessment of forest inventory and river path analysis. Vegetation indices are intended to improve the vegetation signal, while trying to minimize the solar irradiance and soil background effects. Even though the NDVI
Arab J Geosci Table 1
Detail information for satellite image of Jabalpur region
Main information
Scene corners
At scene center
Orbit info
Satellite: IRSP6
North West Latitude 23.298
Latitude 23.163
Pass type: PLD
Other information
Sensor: L4MX Orbit: 33078 Segment: 1
Scene Center Time: 061053247682 North West Longitude 80.011 Longitude 80.102 Imaging Orbit Number 33078 Time 061053247682 North East Latitude 23.245 Sat Heading Angle 194.115 Dumping Orbit Number 33078 Scene ID : 1130800201 North East Longitude 80.249 Sun Azimuth 144.088 Tilt Angle(Deg): 5.575
Strip: 2 Scene: 036
South East Latitude 23.028 Sun Elevation 53.308 South East Longitude 80.193
Nadir Path: 099 Band2 Gain: 3 Band3 Gain: 3 Band4 Gain: 2 Image Heading Angle: 194.691 Scene Start Time: 061053245838 Scene End Time: 061053249526
Date: 02-MAR-2010 South West Latitude 23.081 Pass Type: PLD Gnd South West Longitude 79.956 Orbit: 3078
indices were introduced to detect the chlorophyll signal only, the soil background, moisture condition, solar zenith angle, view angle, in addition to study environmental condition by using the different index values. However, the NDVIs have been employed extensively to examine the relation among spectral variability and the vegetation growth rate. Basically, NDVI technique demonstrates the relation between the absorption of red radiation using vegetation chlorophyll and the strong scatter of NIR radiation (Beck et al. 2006). NDWI Satellite image processing methods offer essential facilities to map surface water features and observe the dynamics of surface water. In NDWI method, the spectral water index is a single number which is computed from an arithmetic procedure that is commonly known as ratio, difference, and normalized difference of two or more spectral bands. A suitable threshold value for NDWI index is then settled to classify water bodies from other land cover features on the basis of the spectral qualities. The formation of a spectral water index was focused around the way that water absorbs energy at NIR wavelengths. The mathematics procedure not only improves the spectral signals by contrasting the reflectance between different wavelengths, but also abandoned a huge amount of the noisy elements which are usual in numerous wavelength zones. Reflectance ratio of water body in NIR band is much lower than that in visible band. So, NDWI is used to extract water body. The similar procedure has been adopted to formulate the NDWI (Gao 1996), defined as NDWI ¼
Green −N IR ; Green þ N IR
ðwhere −1 < NDWI < 1Þ
ð5Þ
where green and NIR are the reflectance of green and NIR bands, respectively. In 1996, the researcher Gao (1996) has set the NDWI range to zero as the threshold criteria. That is, the
cover type is water if NDWI >0, and it is nonwater if NDWI ≤0. The water information could be extracted with appropriate threshold value according to NDWI. As a side note, Gao (1996) created an alternate NDWI utilized for assessing water substance of vegetation canopy. In spite of the fact that McFeeters (1996) and Gao’s NDWI have the same wording, the ideas of the two NDWI are totally different. In Gao’s paper, NDWI was estimated as the normalized difference of NIR and near infrared, or shortwaveinfrared (SWIR) bands. In 2004, Rogers and Kearney (2004) utilized red and SWIR band to create NDWI, which is expressed given by NDWI ¼
Red −SWIR ; Red þ SWIR
ðwhere −1 < NDWI < 1Þ
ð6Þ
where red is the reflectance of the red band, and SWIR is the reflectance of the SWIR band. In this study, we dissected the variety of the thresholds for diverse NDWI using spectral data acquired from a spectral library. Particularly, our investigates were centered around (a) the impact of soil and vegetation subpixel components on the variation of NDWI thresholds, and (b) the performance of all distinctive forms of NDWI focused around sensitivity of the index to the subpixel components. SVD In linear algebra, the SVD (Bhandari et al. 2014d) is an essential factorization of a rectangular real or complex matrix with numerous applications in signal/image processing and statistics. SVD can be calculated mainly by the three mutually compatible points of view. In other words, SVD is a method for transforming correlated variables into a set of uncorrelated ones that better expose the various relationships among the original data items (Rufai et al. 2014). At the same time, it is also a method for identifying and ordering the dimensions
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along which data points exhibit the most variation. Hence, SVD can be seen as a method for data reduction and mostly for feature extraction as well as for the enhancement of the low-contrast images. The following are the basic ideas behind SVD: taking a high dimensional, highly variable set of data points and reducing it to a lower dimensional space that exposes the substructure of the original data more clearly and orders it from most variation to the least (Bhatnagar and Raman 2009; Aslantas et al. 2008). Utilizing of SVD within digital image processing has a few advantages. First, the size of the matrices for SVD transformation is not fixed. It can be a square or rectangle. Second, singular values in an image are less affected if general image processing is accomplished (Bhatnagar and Jonathan 2012). An image is basically a matrix of numbers, in which the elements represent the intensity value of corresponding pixels of the image. SVD is used in order to factorize a particular matrix into three matrices acknowledged as U, S, and V in which U and V are orthogonal and S is a diagonal matrix containing the sorted singular values of the input matrix in descending order (Rufai et al. 2014). In other words, SVD can be expressed by the simple formulation using Eq. (7). Let A be a general real (complex) matrix of order m × n. The mathematically SVD of X can be described as follows: X ¼ U *S*V
T
ð7Þ
where U and V denote orthogonal (unitary) matrix and S represents [diag(σ1,σ2, . . . ,σr )], where, σi, i=1(1)r represents the singular values of the matrix X with r=min(m,n) and fulfilling σ1 ≥σ2 ≥···≥σr. The number of nonzero elements on the diagonal of S matrix determines the rank of the input matrix X. S obtained by eliminating small singular values (σr) to approximate the original matrix. Singular matrix has sorted singular values which arranged in descending order. In the factorized matrix, the first r columns of V are known as right singular vectors and the first r columns of U are called the left singular vectors. General histogram equalization General histogram equalization (GHE) is widely used for contrast enhancement in countless applications such as most commonly used in medical image processing, radar signal processing, and satellite image processing due to its straightforward function and effectiveness. In general, the GHE technique enhanced the contrast of given images in accordance with the sample distribution of an image. Basically, GHE flats the density distribution of the output image and improves the contrast of the image as a result, since histogram equalization
has a response of stretching dynamic range (Gonzalez et al. 2004; Kim 1997). Let X={X(i , j)} represents a given image composed of L discrete gray levels indicated as {X0, X1, . . . XL-1}, where X(i, j) denotes an intensity of the image at the spatial location (i, j) and X(i , j)Є{X0, X1, . . . XL-1}. The probability of occurrence of gray level Xk in an image is approximated by pð X k Þ ¼
nk ; k¼0; 1; …; L−1 n
ð8Þ
where n is the total number of pixels in the image, nk is the number of pixels that have gray level Xk, in the input image X and L is the total number of possible gray levels in the image. Note that p(Xk) is associated with the histogram of the input image which denotes the number of pixels that have a specified intensity region Xk. Actually, a plot of nk versus Xk is recognized as the histogram of X. The discrete version of the transformation function is expressed as sk ¼ T ðr k Þ ¼
k X
k X nk pr r j ¼ ; k ¼ 0; 1; … ; L−1: ð9Þ n j¼0 j¼0
Hence, a processed (enhanced) image is achieved by the mapping each pixel with level rk in the input image into a corresponding pixel with level sk in the output image via Eq. (9). The transformation (mapping) specified in Eq. (9) is known as histogram equalization or histogram linearization. In view of probability density function (PDF), the cumulative density function (CDF) can be defined as cðxÞ ¼
k X p X j ; where X k ¼x; for k¼0; 1; … ; L−1 ð10Þ j¼0
Note that c(XL-1)=1 according to definition of CDF. GHE is a method that maps the input image into the entire dynamic range, (X0, XL-1), by using the CDF as a transform function. A transfom function f(x) based on the CDF is expressed as f ðxÞ ¼ X 0 þ ðX L−1 −X 0 ÞcðxÞ
ð11Þ
In common practice, the enhanced image based on the histogram equalization process can be accomplished using the following expression as Y ¼ f ðX Þ ¼ f f ðX ði; jÞÞj∀X ði; jÞ∈X g
ð12Þ
where Y represents {Y(i, j) which provides the histogram equalized image.
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Proposed methodology The proposed work of enhancement of color satellite image is carried out in two parts. The first one is the use of SVD. As it was mentioned in the previous section, the singular value matrix obtained by SVD contains the illumination information. Therefore, altering the singular values will directly affect the illumination of the image; hence, the rest of the information will remain the same. Second most significant aspect of this method is the application of DWT. In the proposed technique, at the outset low contrast input colored satellite image ‘Ai’ is processed by GHE to generate ‘Ai^’. After getting this, both of these images are transformed by DWT into the four frequency subband, LL, LH, HL, and HH. Then, the correction coefficient for singular value matrix can be calculated by using max ∑LLAi ^ ξ¼ ð13Þ max ∑LL
and NDWI process needs to separate each and every band, which is present in the satellite image. After separation of different band, NDVI and NDWI methods are applied according to its characteristic like vegetation and water bodies at different threshold values such as 0.2, 0.4, 0.6, and 0.7. Creation of false color composite (FCC) images The alternation of the color for an image, like red light for blue, green for red, blue for green, is called false color image or FCC. Basically, false color composite is used to identify some specific feature by changing their color.
Low contrast input colored satellite image
Equalized image by GHE
Ai
where i € {R, G, B} i.e. AR, AG, AB, and ∑LLAi is lowerfrequency coefficient singular matrix of the input image, and ∑LLAi^ is lower-frequency coefficient singular matrix of the satellite output image of the GHE. The new satellite image (LL) is examined by ∑LLAi ¼ ξ∑LLAi
DWT of (Ai) HH
HL
LH
DWT of (Ai) LL
LL
SVD, Calculate and find the max element in
Calculate using LL
Ai ^
=
max
LLAi ¼ U LLi ∑LLAi V LLi Calculate the new
Now, LLAi ; LH Ai ; HLAi &HH Ai subband images of the original image are reorganized by applying the IDWT to produce the consequence-equalized image Āi as given by Eq. (15) Ai ¼ IDWT LLAi ; LH Ai ; HLAi & HH Ai ð15Þ
HL HH
SVD, Calculate and find the max element in
max
ð14Þ
LH
LLAi
LLAi & LL Ai
using Eq. (14)
IDWT Equalized DWT-SVD satellite image Separation of NIR band, red band, green band from the proposed equalized DWT-SVD images Apply NDVI and NDWI technique on the DWT-SVD based enhanced images
The mechanism of contrast enhancement can be attributed to scaling of singular values of the DWT coefficients. Since singular values denote luminance of each image layer after decomposition, scaling of these values leads to variation (enhancement) of luminance of each layer, and hence leads to overall contrast enhancement.
Vegetation and water region detection using NDVI and NDWI method Image of vegetation and water detection at 0.1, 0.2, 0.3, 0.4, 0.5 & 0.6 threshold values from enhanced DWT-SVD images FCC images for vegetation and water indices at best suited NDVI and NDWI value, extracted from enhanced DWT-SVD images
Finding vegetation in a multispectral satellite image END
For the detection of the vegetation and water index from a preprocessed multispectral remote sensing image, the NDVI
Fig. 1 Flowchart of the proposed DWT-SVD and NDVI based methodology
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The following subsections discuss the main computational process of the proposed algorithm: Step 1: In the very first step, a low-contrast colored input satellite image has been taken for the analysis. Step 2: Equalize the satellite image using GHE. Step 3: In this step, DWT transforms the original image into four subbands, such as LL, LH, HL, and HH. The calculation of four subbands is processed in two parts as: (i) First equalized by GHE and then, apply DWT into the low-contrast satellite image. (ii) Directly applied DWT into the low-contrast satellite image. Step 4: After getting LL, LH, HL, and HH, SVD is applied for of the U, Σ, V and find the max element in Σ. Step 5: Calculate max ∑LLAi^ and max ∑LL with the Ai help of SVD process. Step 6: Calculate ξ using Eq. (5). Step 7: Calculate the new ∑LLAi and LLAi using Eq. (14). Step 8: Apply IDWT after getting new ∑LLAi . Step 9: Equalized DWT-SVD satellite image. Step 10: Separation of NIR, red, and green band from DWTSVD preprocessed satellite images. Step 11: Calculate NDVI and NDWI image from DWTSVD processed satellite images. Step 12: Vegetation and water region detection using NDVI and NDWI methods. Step 13: Image of vegetation detection at 0.2, 0.4, 0.6, and 0.7 NDVI threshold values from DWT-SVD processed images. Step 14: Image of water portion at 0.1, 0.2, 0.3, 0.4, 0.5, and 0.6 NDWI threshold values from DWT-SVD processed images. Step 15: Creation of FCC image from equalized DWT-SVD images for vegetation and water region analysis. A complete flowchart routine for the proposed method is shown in Fig. 1.
Table 2 Thematic bands of NASA are as LANDSAT satellite
Result and discussion In this section, the experimental results are presented, and different satellite images (MATLAB Image Processing Toolbox User Manual, NASA Earth Observatory images, Mississippi River 2008; NASA Earth Observatory images, Yellowstone National Park 2009) are used for better analysis of results. In this paper, Meyer (dmey) wavelet is used as a mother wavelet, and only one level of subband decomposition is used for better enhancement and to avoid the image degradation. The performance of the proposed enhancement technique is evaluated by considering fidelity of enhanced image to the original image. For this purpose, mean and standard deviation fidelity assessment parameters are considered. Not only visual comparison but also quantitative comparisons are validating the superiority of the proposed method. Performance of this method is measured in terms of following significant parameters: M −1 N −1 1 XX I ðx; yÞ M N x¼0 y¼0
ð16Þ
M −1 N −1 1 XX Variance σ2 ¼ fI ðx; yÞ−μÞg2 M N x¼0 y¼0
ð17Þ
MeanðμÞ ¼
Mean (μ) is the average of all intensity value. It denotes average brightness of the image, where as standard deviation is the deviation of intensity values about mean. It denotes average contrast of the image. Here, I(x, y) is intensity value of the pixel (x, y), and (M, N) are dimensions of the image. For a remote sensing image, the mean reflects average brightness of remote sensing images. If mean is moderate (gray value in the vicinity of 128), it indicates good visual effects. Any pixel of an image can be considered as a random variable with a distribution function. At first, performance of the proposed algorithm is carried out on the multispectral satellite image sample. Thereafter, comparison of the proposed method is done over decorrelation stretching which shows superiority of the proposed technique as shown below.
Band no.
Name
Wavelength (μm)
Characteristics and use
1. 2. 3. 4. 5. 6. 7.
Visible blue Visible green Visible blue Near infrared Middle infrared Thermal infrared Middle infrared
0.45–0.52 0.52–0.60 0.63–0.69 0.76–0.90 1.55–1.75 10.4–12.5 2.08–2.35
Maximum water penetration Good for measuring plant vigor Vegetation discrimination Biomass and shoreline mapping Moisture content of soil Soil moisture, thermal mapping Mineral mapping
Arab J Geosci
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
(m)
(n)
(o)
(p)
Fig. 2 a The low-contrast satellite images of Paris (France) region. b Equalized image by the decorrelation stretch technique. c, d, e Separated NIR, red, and green band from the decorrelation stretch enhanced image. f The histogram of the decorrelation stretch output image. g The NDVI image applied on low-
contrast input image. h The NDVI image of decorrelation stretch enhanced image. i, k, m, o The vegetation index image at 0.2, 0.4, 0.6, and 0.7 threshold value and j, l, n, p their FCC images which are extracted from decorrelation stretch enhanced image
Arab J Geosci
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
(m)
(n)
(o)
(p)
Fig. 3 a The low-contrast satellite images of Paris (France) region. b The equalized image by proposed DWT-SVD technique. c, d, e Separated NIR, red, and green band from the proposed DWT-SVD enhanced image. f Histogram of DWT-SVD output image. g The NDVI image applied on low-contrast input image.
h The NDVI image of DWT-SVD enhanced image. (i), (k), (m) and (o) are vegetation index image at 0.2, 0.4, 0.6 & 0.7 threshold value. j, l, n, p Their FCC images which are extracted from DWT-SVD enhanced image
Arab J Geosci Table 3
Comparison of results between decorrelation technique and DWT-SVD output images
Sample image
Input image Mean (μ) and standard deviation (σ)
Decorrelation stretch technique Mean (μ) and standard deviation (σ)
DWT-SVD image based technique Mean (μ) and standard deviation (σ)
1.
μ=54.9985 σ=249.0432 μ=96.5763 σ=3.6017e+003 μ=108.4103 σ=1.7183e+003 μ=162.0180 σ=1.0767e+003
μ=105.5832 σ=2.2472e+003 μ=124.1478 σ=5.6342e+003 μ=121.3243 σ=3.4957e+003 μ=124.5170 σ=2.5006e+003
μ=128.7597 σ=4.4716e+003 μ=131.0538 σ=5.6084e+003 μ=123.7350 σ=4.3599e+003 μ=127.1118 σ=4.1846e+003
2. 3. 4.
Additional information of image dataset The satellite data used in this manuscript has been taken from different resources; therefore, the nature of each satellite images is different. Satellite image 1 Fig. 4a (MATLAB Image Processing Toolbox User Manual, Paris image) is a Thematic Mapper image which is covering part of Paris, France, made available courtesy of Space Imaging. In this image, seven spectral bands are stored in one file in the Erdas LAN format. This example shows how differences between the visible red and NIR bands of a LANDSAT image can be used to identify areas containing significant vegetation. Thematic Mapper 4, 3, and 2 bands cover the NIR, the visible red, and the visible green parts of the electromagnetic spectrum. When they are mapped to the red, green, and blue planes, respectively, of an RGB image, the result is a standard color-infrared (CIR) composite. It is an RGB image, but with false colors. When the image is displayed, red signifies the NIR band, green signifies the visible red band, and blue signifies the visible green band. In the CIR image, water features are very dark (the Seine River), and green vegetation appears red (parks and shade trees). Overall, the contrast is low, and the colors are subtle. Satellite image 2 Fig. 4b (NASA Earth Observatory images, Mississippi River, 2008 http://earthobservatory.nasa.gov/ NaturalHazards/view.php?id=20046) is NASA image. This trio of images shows just how quickly floods rose on the Mississippi River in mid-June 2008. The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite captured the top false color image on June 19, 2008. By June 19, the flood area expanded to include several new areas. The river spread across parts of Indiana and Missouri near the city of Alexandria, Missouri. A burst levee allowed water to flood the town of Gulfport, Illinois. The floods were beginning on June 17, 2008 but had expanded significantly by June 19. This type of false color satellite image is particularly useful for monitoring floods because it increases the contrast between land and water, which is often muddy brown in photolike, natural color images. The images combine infrared
and visible light so that water is black or dark blue. Bare Earth or lightly vegetated land is tan-pink, and plant-covered land is bright green. Clouds are pale blue and white. Satellite image 3 Fig. 4c (NASA Earth Observatory images, Yellowstone National Park 2009 http://visibleearth.nasa. gov/view.php?id=39125) track one fire in western Yellowstone and the landscape’s gradual recovery over a period of 20 years; they are part of a larger collection of images featured in Burn Recovery in Yellowstone, the latest installment in the Earth Observatory’s ongoing World of Change series. The Landsat-5 satellite acquired this series of images between 1987 and 2008. The images were made with a combination of visible and infrared light (green, short-wave infrared, and NIR) to highlight the burned area and changes in vegetation. This image shows the park as it had been for many years. Dense stands of tall, straight pine trees covered the high, volcanic plateau. The old forest is dark green in the image. Mineral-rich geyser fields are pale blue and white, and lakes are dark blue. Satellite image 4 Fig. 4d represents the low-contrast satellite image of Jabalpur region. The detail information of this satellite image is given in Table 1. Practical implications Image enhancement is a decisive and fundamental step for remote sensing information retrieval and classification. In this paper, an improved feature extraction scheme for satellite images using NDVI and NDWI Technique based on DWT and SVD has been proposed. This method is considered for satellite images, and remotely sensed images are having some Fig. 4 a–d Various low-contrast original satellite images, e–h corresponding decorrelation stretched improved images, i–l corresponding vegetation index at 0.2 NDVI threshold value using decorrelation stretched improved images, m–p corresponding DWTSVD equalized images, q–t corresponding vegetation index at 0.2 NDVI threshold value using DWT-SVD equalized images
Arab J Geosci
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Arab J Geosci Table 4 The value of the performance indicator obtained for NDVI by proposed and existing techniques
Sample image
NDVI threshold values
Vegetation extracted from proposed DWT-SVD enhanced image
0.2
14.9487 %
18.5078 %
34.8438 %
2 3 4
0.4 0.6 0.7 0.2 0.2 0.2
5.2185 % 0.8080 % 0.0027 % 0.0492 % 1.4492 % 32.0760 %
10.0601 % 5.5000 % 3.8754 % 8.6825 % 9.4334 % 21.1774 %
21.5603 % 12.7678 % 9.5943 % 45.7158 % 43.5505 % 17.7438 %
Sample image
1
2
3
Bold entries represents the best NDWI threshold value and appropiate percentage of extracted water region for perticular image
Vegetation extracted from decorrelation stretching enhanced image
1
band combination such as in case of INSAT image it is having 3 band data, and in case of LANDSAT images, it is having seven band data. In this study, one INSAT and three LANDAT images with different features are used for better comparison of the results. Generally, any kind of practical processing of satellite images has been performed in the three bands named NIR, red, and green bands. The specification of the all band combination is given in Table 2. Owing to the presence of different bands with different wavelength region in the satellite images, efficiency of the algorithms is effected and containing high resolution is one more cause of inefficiency as a result it leads to computational complexity during feature extraction process. In case of satellite images, precisely finding the object of interest in the existence of inherent uncertainty and ambiguity is a challenging task in satellite image processing. Table 5 The different value of the performance indicator obtained for NDWI by proposed and existing techniques
Vegetation extracted from low contrast input image
4
NDWI threshold values
Study of experiment results After analysis of both the techniques, it has been found that the quality of input image was poor, but after applying DWTSVD, result is optimized with reference to brightness and contrast. And thereafter, NDVI technique is applied to get more accurate result in case of vegetation region. After applying NDVI technique over Fig. 2b satellite image, it has been found that at 0.2 NDVI thresholds value only 18.5078 % of vegetation has been detected. When the threshold value is varied to 0.7, then the technique is giving very less (3.8754 %) vegetation areas. While same procedure is applied over Fig. 3b satellite image then, it is found that the NDVI technique is capable to give more efficient amount of vegetation index. The proposed enhancement based NDVI technique is giving 34.8438 % at 0.2 NDVI thresholds value and
Water portion extracted from low contrast input image
Water region extracted from enhanced image using Decorrelation Stretching method
Water region extracted from proposed DWT-SVD enhanced image
0.1
1.5511
52.1141
12.6759
0.2 0.3 0.4 0.1 0.2 0.3 0.4 0.3 0.4 0.5 0.6 0.1 0.2 0.3 0.4
0.3185 0.0809 0.0175 7.4382 3.9984 2.4843 1.8380 20.4102 11.5940 7.4341 6.3320 0.1111 0.00 0.00 0.00
44.7281 37.3203 27.8603 13.2744 10.7341 8.8402 7.5824 20.1466 15.2931 10.9165 7.1239 39.0261 22.8498 11.6037 5.4467
9.3033 6.6822 4.6875 17.1174 11.6151 9.1190 7.7459 23.4669 17.6006 12.5298 9.2083 40.1032 28.2302 18.9048 12.4598
Arab J Geosci
9.5943 % of vegetation at 0.7 NDVI thresholds value while previous technique is giving very less at the same NDVI thresholds value, and histograms obtained from of the proposed technique are also stretched in dynamic range thereby signifying the improvement in contrast of the output image. The proposed DWT-SVD based feature extraction method represents better contrast as well as better brightness which are caused of better vegetation portion for all four satellite images. In this paper, four sample images are used for better comparison of equalized results and extracted vegetation features. The various sample images indicate that the proposed technique is true for all four satellite images. After examination of each method, it can be revealed that the proposed technique indicates better mean (μ) and standard deviation (σ) in comparison with the decorrelation stretching as shown in the Table 3. The quality of the input image was
poor, but after applying DWT-SVD, result is optimized with reference to brightness and contrast. The histograms obtained from the proposed technique are stretched in dynamic range thereby signifying the improvement in contrast of the output image. It is clear from the distributions that the estimated Gaussian functions of the proposed method have means which are close to the ideal mean [Fig. 4m, n, o, and p with μ= 128.7597, μ=131.0538, μ=123.7350, and μ=127.1118]. The proposed DWT-SVD represents the better contrast as well as better brightness with suitable edges. The estimated mean (μ) and standard deviation (σ) in Fig. 4m, n, o, and p of the proposed method yields that the proposed method covers a fine range of gray levels. The region behind superior illumination and very clear visualization is basically due to proper stretching of the intensity value [0, L-1]. The enhanced result raise performance level of intensity and contrast of the
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Fig. 5 a–d Corresponding water index at 0.1, 0.2, 0.3, and 0.4 NDWI threshold value using input image directly, e–h corresponding water index at 0.1, 0.2, 0.3, and 0.4 NDWI threshold value using
decorrelation stretched improved images, i–l corresponding water index at 0.1, 0.2, 0.3, and 0.4 NDWI threshold value using DWT-SVD equalized images
Arab J Geosci
multispectral images, as a consequence of which, we can easily identify the specific features presented in the satellite images. Table 4 is representing superior performance of the preprocessing based on NDVI technique. It is denoting that lowcontrast input image is giving very less vegetation area at various NDVI threshold value. In case of low-contrast images, the NDVI is unable to detect exact features due to the pixel overlapping, poor contrast, and some other problem during processing of the images. But, after enhancement of images, features are clearly visible, and vegetation area easily interpreted by NDVI technique as shown in Fig. 4q, r, s, and t. The quality of the visual results indicates that the proposed equalization technique is sharper and brighter than as compared to the existing technique. From the fidelity assessment
as shown in the Table 4, it is found that the proposed algorithm gives better result in comparison with decorrelation-based feature extraction technique. Table 5 indicates improved performance of the preprocessing based NDWI technique. It describes the drastic change in feature extraction process using suitable enhancement approach. It is noted that the NDWI method which is calculated directly from input image was either giving very less or distorted amount of water region at various NDWI threshold value. However, improved classification scheme which is based on enhancement of satellite image is providing better performance in the feature. In addition, the visualized results are given in Figs. 5, 6, 7, and 8 at numerous thresholds. Table 5 represents that the NDWI threshold value vary image to image and features present in the image. It can be notice that the third
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Fig. 6 a–d Corresponding water index at 0.1, 0.2, 0.3, and 0.4 NDWI threshold value using input image directly, e–h corresponding water index at 0.1, 0.2, 0.3, and 0.4 NDWI threshold value using
decorrelation stretched improved images, i–l corresponding water index at 0.1, 0.2, 0.3, and 0.4 NDWI threshold value using DWT-SVD equalized images
Arab J Geosci
sample satellite image is giving high (0.4) NDWI threshold value. The region behind giving high threshold value is due to presence of more water bodies. The simulated NDWI threshold values were found to be influenced by the fractions of soil and vegetation at a given water fraction (>0 %). Additionally, it is found that the NDVI technique is very sensitive to the subpixel vegetation elements; due to this, NDVI is not appropriate for delineating water region. In the application of mapping surface water bodies using the NDWI system, the NDWI threshold ought to be adjusted to match a reference information set that has a better spatial determination. The proposed technique presents a simple formulation of improved classification scheme for NDVI and NDWI technique using DWT and SVD for satellite images. The overall procedure is straightforward with very less mathematical
calculation. As a result, the execution time to enhance and classify the satellite image is very less. Explanation of NDWI and NDVI features The NDWI is a more recent satellite-derived index from the NIR and short wave infrared (SWIR) channels that reflects changes in both the water content (absorption of SWIR radiation) and spongy mesophyll in vegetation canopies. For that reason, continued evaluation of satellite-derived NDVI and NDWI is required to better understand how these indices respond to for vegetation and water bodies, which are ultimately tied to drought stress on plants. Figures 4, 5, 6, 7, and 8 show the relationship between NDVI and NDWI for individual pixels under different climate conditions over the different level of NDVI and NDWI threshold values. Results indicated
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Fig. 7 a–d Corresponding water index at 0.3, 0.4, 0.5, and 0.6 NDWI threshold value using input image directly, e–h corresponding water index at 0.3, 0.4, 0.5, and 0.6 NDWI threshold value using
decorrelation stretched improved images, i–l corresponding water index at 0.3, 0.4, 0.5, and 0.6 NDWI threshold value using DWT-SVD equalized images
Arab J Geosci
through green colors for water index in Figs. 5, 6, 7, and 8 suggest that the NDWI was more sensitive than NDVI to drought conditions. The NDVI and NDWI values decreased considerably more during the calculation through the lowcontrast satellite images which indicates that these techniques were unable to determine appropriate features from the raw satellite images. In Table 5, different threshold values are considered to extract the water index at different levels using NDWI technique. In this table, comparison of direct input image based water index and proposed improved feature extraction based results are presented. It can be noticed that in case of fourth satellite image, NDWI index is found almost zero for each level of threshold NDWI values. This indicates that without effective preprocessing stage, appropriate feature extraction is not possible for remote sensing images. In order to get
accurate feature of water regions, combination of the proposed enhancement method and NDWI techniques are utilized which clearly indicates superior amount of the water index such as 40.1032, 28.2302, 18.9048, and 12.4598, respectively, whereas input image (0.1111 and 0) and decorrelation stretching methods give 39.0261, 22.8498, 11.6037, 5.4467 water index, respectively. The NDVI has been most popularly used index for vegetation detection from remote sensing data for many years. This concept is based on radiances or reflectances from a red channel around 0.66 μm and a near-IR channel around 0.86μm. In this method, red channel is positioned in the strong chlorophyll absorption region, while the near-IR channel is positioned in the high reflectance plateau of vegetation canopies. These two channels sense extremely miscellaneous depths through vegetation canopies. As expressed
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Fig. 8 a–d Corresponding water index at 0.1, 0.2, 0.3, and 0.4 NDWI threshold value using input image directly, e–h corresponding water index at 0.1, 0.2, 0.3, and 0.4 NDWI threshold value using decorrelation stretched improved images, i–l corresponding water index at 0.1, 0.2, 0.3, and 0.4 NDWI threshold value using DWT-SVD equalized images
Arab J Geosci
in Eq. (6), NDWI can be demonstrated in another words such as (ρ (0.86μm) − ρ (1.24μm)) (ρ(0.86μm) + ρ (1.24μm)), where ρ stand for the radiance in reflectance units. In this index, both the 0.86-μm and the 1.24-μm channels are placed in the high reflectance plateau of vegetation canopies. They used to sense equivalent depths via vegetation canopies. Absorption by vegetation liquid water near 0.86μm is negligible. Weak liquid absorption at 1.24μm is present. Canopy scattering improves the water absorption. Due to this behavior of channels, NDWI is sensitive to changes in liquid water content of vegetation canopies. On the other hand, atmospheric aerosol scattering effects in the 0.86-1.24μm region are weak. NDWI is less sensitive to atmospheric effects than NDVI, NDWI does not remove completely the background soil reflectance effects, similar to NDVI. Due to information of vegetation region are included in the 1.24-μm channel is very diverse from that covered in the red channel near 0.66μm, NDWI should be considered as an independent vegetation index. It is complementary to, not a substitute for NDVI (Gao 1996).
Conclusion In this paper, an improved classification scheme for NDVI and NDWI technique using DWT and SVD has been proposed for vegetation and water body extraction from satellite images. The basic enhancement occurs due to scaling of the singular values of DWT coefficients. The experimental results show that the proposed technique gives better performance in terms of vegetation and water indices as well as visual perception. Thus, this technique can be considered suitable for enhancement and feature extraction of low-contrast satellite image. The NDVI and NDWI methods give superior results for vegetation and water varying in densities. The simulation results show that the enhancement based NDVI and NDWI using DWT-SVD technique is highly useful to detect the surface features of the visible area which are enormously beneficial for the municipal planning and management. The vegetation and river path analysis can be used in the situation of unfortunate natural disasters to provide humanitarian aid, damage assessment and furthermore to device new protection strategies. In remote sensing image enhancement, it has broad application prospects. The method is simple yet flexible with less calculation. Visual examination of the enhancement results reveals the improved feature extraction technique effectively distinguishes better vegetation and water region due to improved contrast image. Hence, it can be useful for various remote sensing applications and also significant to detect surface features of the visible area. Moreover, the proposed method also has the advantages of faster computing speed with effective accuracy of feature extraction. It has great
prospective in research and application of remote sensing image enhancement.
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