J Indian Soc Remote Sens (March 2013) 41(1):35–43 DOI 10.1007/s12524-011-0199-7
RESEARCH ARTICLE
Wavelet Based Post Classification Change Detection Technique for Urban Growth Monitoring R. A. Alagu Raja & V. Anand & A. Senthil Kumar & Sandeep Maithani & V. Abhai Kumar
Received: 2 November 2011 / Accepted: 26 December 2011 / Published online: 20 March 2012 # Indian Society of Remote Sensing 2012
Abstract Urban areas are the most dynamic region on earth. Their size has been constantly increased during the past and this process will go on in the future. Since there is no standard policy and guidelines for construction of buildings and urban planning, cities tend to have irregular growth. Many cities in the world face the problem of urban sprawl in its suburbs. So issues of urban sprawl need to be settled with the help of technologies such as satellite remote sensing and
R. A. A. Raja (*) Remote Sensing & GIS Lab, Thiagarajar College of Engineering, Madurai 625015, India e-mail:
[email protected] V. Anand Remote Sensing, Chennai, India e-mail:
[email protected] A. S. Kumar National Remote Sensing Centre, Indian Space Research, Organisation (ISRO), Hyderabad 500 037, India e-mail:
[email protected] S. Maithani HUSAG, Indian Institute of Remote Sensing, Dehradun, India e-mail:
[email protected] V. A. Kumar Thiagarajar College of Engineering, Madurai 625015, India e-mail:
[email protected]
automated change detection. This paper presents a wavelet based post classification change detection technique that is applied to 1996 and 2004 MSS images of Madurai City, South India to determine the urban growth. The classification stage of the technique uses coilflet wavelet filter to correlate with the MSS land cover images of Madurai city to derive texture feature vector and this feature vector is inputted to a fuzzy-c means classifier, an unsupervised classification procedure. The post classification change detection technique is employed for identifying the newly developed urban fringe of the study area. The error matrix analysis is used to assess the accuracy of the change map. The performance of the presented technique is found superior than that of classical change detection methods such as image differencing, change vector analysis and principal component analysis. Keywords Wavelet transform . Feature extraction . Classification . Change detection . Accuracy assessment
Introduction Remote Sensing technology provides powerful techniques to monitor environmental and land use/cover changes. In the past few years, there has been a growing interest in the development of change detection techniques for the analysis of multitemporal remote sensing data (Singh 1989). This interest stems from the wide range of applications in which change detection
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methods can be used, like urban planning, agricultural surveys, forest monitoring and environmental monitoring (Lindsay et al. 1996 and Niedermeier et al. 2000) etc. Change detection in remote sensing aims at identifying changes of two registered, aerial or satellite multispectral images from the same geographical area obtained at two different times. Change detection uses multitemporal image data sets in order to detect land cover changes caused by short-term phenomena such as flooding and seasonal vegetation change, or long-term phenomena such as urban development and deforestation (Melgani et al. 2002). The change detection procedures are of two types and they are pre classification change detection and post classification change detection techniques. The former gives the amount of change whereas the latter gives the amount and type of change (Mrril and Jiajun 1998). In post classification comparison technique, the change detection accuracy depends on the classification accuracy. Since features in remotely sensed data are often highly heterogeneous, the conventional automatic per-pixel classification procedures cannot always provide a good discrimination between the features (Mas 1999) which results in poor classification accuracy. In order to obtain the features with good discriminatory power the texture measures can be used for classification. Texture is an important feature for the analysis of many types of images. It can be seen in all images of multispectral scanner images obtained from aircraft or satellite platforms. Texture classification is the process by which features are extracted from a set of texture classes (Chan et al. 2000). Most of the conventional approaches for texture feature extraction lead to make use of statistical techniques in which processing the texture image data requires large storage space and computational load to calculate the feature’s matrix content (Laws 1980). The scalar features calculated from this matrix is not efficient to represent the characteristics of image content. Alternatively, spacefrequency analysis has received a lot of attention which uses multi-resolution techniques such as wavelet transform. The space-frequency domain techniques will serve better in extracting texture features. From the recent literatures, these methods have shown significant potential for texture description, where advantage is taken on the spatial-frequency concept to maximize the simultaneous localization of energy in both spatial and frequency domains. Also recent developments in the wavelet domain provide good multi resolution
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analytical tool for texture analysis and can achieve high classification accuracy. This paper presents a novel method for urban growth monitoring which uses wavelet based unsupervised texture classification algorithm. The texture features from the two multitemporal images are extracted by wavelet frames and thereby classified using Fuzzy C-means algorithm. The two independently classified images are then subjected to post classification comparison change detection technique to obtain the urban growth map.
Study Area & Data Used Study Area The selected area for this research is the Madurai city in south India. Madurai is a historical city and is one of the mini metros in India. The city is well known for its architectural marvels and rich cultural heritage. The city is known as the Athens of the east. Madurai is situated at a longitude of 780 04’ 47” E to 780 11’ 23” E and a latitude of 90 50’ 59” N to 90 57’ 36” N. The topography of Madurai is approximately 101 m above mean sea level. The present area of the city is 51.9 sq.km. Data Used Two satellite images captured by IRS-1B and IRS-P6 (Indian Remote sensing Satellites) at different periods (March 1996 & March 2004) are taken for analysis and they are shown in Fig. 1. These images are subjected to geometric correction with the help of Ground Control Points (GCPs) and both the images which are of different spatial resolutions are re-sampled to 30 m by 30 m spatial resolution. There are five major classes in which the regions of Madurai images can be classified. They are features like urban, vegetation, water body, hilly region and waste land. The urban category covers residential, industrial/commercial, transportation, recreational, public/semi-public, mixed built-up land. The vegetation category covers crop land, plantation and forest. The water body category contains canals, ponds and river. The hills category of the study area comprises Pasumalai hills, Thiruparankundram hills, Alagar hills and Yanaimalai Hills Figs. 2 and 3.
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Fig. 1 Satellite images of Madurai (a) IRS 1B- 1996 (b) IRS P6 - 2004
Classical Change Detection Methods
Image Differencing(ID)
Urban sprawl monitoring can be done through analyzing land use changes over time using an approach called change detection. Change detection techniques using remotely sensed data are an important means of identifying the transformation of the geographic landscape. In this paper, three most used classical change detection algorithms namely image differencing, change vector analysis and principal component analysis have been applied on the study area and the change detection accuracies of these methods were evaluated using this error matrix for which about 250 ground truth features from the study area have been collected and used. The results were compared with the proposed wavelet based change detection algorithm.
In image differencing, co-registered images from two dates are subtracted, pixel by pixel, to produce a new image that represents the digital change between the two dates. An optimum threshold value selected through experiments is used to discriminate change from no-change area (Kressler and Steinnocher 1999).
Fig. 2 Change map (a) ID (b) CVA (c) PCA
Change Vector Analysis(CVA) The change vector of a pixel is defined as the vector difference between the multi-band digital vectors of the pixel on two different dates. When a land cover feature undergoes a change, its spectral appearance
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Fig. 3 (a) Scaling function (b) Wavelet function (c) 3-Level decomposition
changes accordingly. The vector describing the direction and magnitude of change from the first to the second date is a spectral change vector (Lambin and Strahler 1994; Johnson and kasischke 1998). In this method, two associated one-band images are computed. The first contains the magnitude of the pixel change vector. The second contains the direction of the change vector. The decision that a change has occurred is made if the magnitude of the change vector exceeds a specified threshold criterion. Principal Components Analysis(PCA) To obtain changed pixels in temporal images one can use the principal component analysis method (Singh 1989). The relation between the spectral signal Xi (T1) and Xi (T2) received from a reflecting surface at two times T1 and T2 is very often modelled approximately as a linear function (Singh 1989, Richards 1993). Based on this linear function, one can plot all pixels along two axes, the first and second principal components. One of the axes represents the unchanged component of the temporal images and the perpendicular one represents the change component. It is expected that all unchanged pixels will lie in a narrow elongated cluster along the first principal axis (PC1). On the other hand, the pixels which have experienced change in their spectral appearance are expected to lie far away from this axis. The mean of the transformed vectors along the change axis is then computed (Collins and Woodcock 1996). By using an optimum threshold, pixels are then classified into change or unchanged pixels.
Proposed Method The methodology proposed in this paper for urban growth monitoring can be graphically described in a block diagram as shown in Fig. 4. Wavelet Transform Wavelet transform is a bound alterable window method. It can get more accurate low frequency information using longer interval and get high frequency information using shorter interval. The advantage of the wavelet transform is that it provides the space frequency information which is important for texture based analysis.
Satellite Data-1
Satellite Data-2
Preprocessing
Preprocessing
Wavelet Decomposition
Wavelet Decomposition
Texture Feature Extraction
Texture Feature Extraction
Texture Classification
Texture Classification
Post Classification Change Detection
Change Map
Accuracy Assessment
Fig. 4 Methodology
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When used as a foundation for a texture measure, the wavelet transform enjoys a number of advantages such as spatial discrimination and multi-scale representation over other methods (Coggins and Jain 1985).When applied to signals, the wavelet transform function decomposes a signal into a set of basis functions (S.G. Mallat 1989). The basis function is, y
a; b
1 xb ðX Þ ¼ pffiffiffiffiffiffi yð Þ a j aj
ð1Þ Texture Feature Extraction Using DWFT
Here a, b are real. Each wavelet function in the decomposition is formed from a ‘mother’ wavelet function y(x) which is scaled (a) and translated (b) and the result being localized in both the frequency and spatial domains. The continuous time wavelet transform of a function f(x) is defined as, Za CTWT ða; bÞ ¼ a
some of the information is lost due to the size reduction of input image. This may be applicable for processing classical images. But for images like remote sensing data, minor information is also important for obtaining higher classification rate. To achieve this, an algorithm by making use of Discrete Wavelet Frame Transform (DWFT) is used in this paper for texture feature extraction.
1 xb Þdx f ðxÞ pffiffiffiffiffiffi yð a jaj
ð2Þ
In practical applications the wavelet transform is applied to discrete signals of finite length for which discrete wavelet transform (DWT) has been developed. DWT is based on the fact that a signal may be represented in two parts an average signal and a detail signal. The smoothed signal is decomposed over a set of basis functions Φ(x) (the scaling function) while the detail signal is over the wavelet basis Ψ(x). Most of previous works on texture extraction have focused on discrete wavelet transform with decimation by which Fig. 5 Filter Bank Structure of DWFT (3- Level Decomposition)
In order to apply wavelets to texture image classification, the two-dimensional discrete wavelet frame transform is generated through the tensor product of 1-D wavelets along the vertical and horizontal directions.The filter bank structure of discrete wavelet frame transform (DWFT) is given in Fig. 5. This method is similar to discrete wavelet transform (DWT), with no down-sampling is done between scales (levels of decomposition). The 3- level decomposition of the satellite data is done in this paper and is shown in Fig. 3(c). The decomposition of the image into different frequency bands is obtained by successive high pass and low pass filtering. First, the low pass filter is applied for each row of the data, getting the low frequency components and the high pass filter is then applied for the same row of data, and the high frequency components are separated. As a result of applying DWFT to an input image, the LL subband image is obtained that contains only low frequency information but has the same image size as the original
Decomposition Level-I Wavelet Basis I
Decomposition Level-II Wavelet Basis II
Decomposition Level –III Wavelet Basis III H2
Approx H1
H0
Input Image
G2
G1
G 0
Details
Details
Details
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Fig. 6 Urban distribution- By proposed method (a) 1996 (b) 2004 (c) Urban sprawl map
image. When it is applied, the detail extracted is finest (i.e., of highest frequency) from the first level of the filter-bank and becomes progressively more coarse. It is this progression that allows the wavelet transform to provide a multi-scale representation of texture (Acharyya and Kundu 2007). This paper uses Coiflet wavelet filters with 3- level decomposition for extracting features. Coiflet is a discrete wavelet designed to be more symmetrical than the Daubechies wavelets. The Daubechies wavelets have N/2−1 vanishing moments whereas the scaling function of Coiflet wavelets have N/3−1 zero moments and their wavelet functions have N/3. The scaling function and the wavelet function of the Coiflet wavelet are given in Fig. 3 and the corresponding coefficients (weights) used for the texture feature extraction are,
Texture Classification
The texture features thus extracted are formed as a feature vector and this feature vector leads to effective discrimination of different textures in the image.
The step after extracting the desired textural parameters is to classify different textures into a map. Texture classification is a major task in the process of satellite image analysis which involves a method of identification of the different texture regions based on the corresponding features (Arivazhagan and Ganesan 2003). For the purpose of texture classification, a feature vector is needed to successfully characterize a textural region. The output from the texture feature extraction stage is a set of images. These images represent different features of the original image. They are used to form feature vectors, where each feature image corresponds to one element in the feature vectors. When texture feature vector is used for classification there is heterogeneity within a texture in one class that causes spectral overlap with texture in other classes. So the traditional approaches like Kmeans clustering algorithm cannot handle data points that are close to more than one cluster. Using fuzzy logic this problem can be overcome (Chanussot et al. 2006). One possible fuzzy clustering which is used in this paper is fuzzy C-means clustering. This is based on finding a good fuzzy partition of the data, which is carried out through an iterative optimization.
Table 1 Error Matrix – ID
Table 2 Error Matrix - CVA
Scaling coefficients 0[-0.0514 0.2389 0.6029 0.2721 -0.0514 -0.0111] Wavelet coefficients0[-0.0111 0.0514 0.2721 0.6029 0.2389 0.0514].
Change Map Data Reference Data
Change
No change
Change MapData Reference Data
Change
62
58
Change
70
52
No change
41
89
No change
37
91
Change
No change
J Indian Soc Remote Sens (March 2013) 41(1):35–43 Table 3 Error Matrix - PCA
41 Table 5 Performance Analysis
Change Map Data Reference Data
Change
No change
Change Detection Method
Accuracy in%
Change
74
42
Image Differencing
60.4
Change Vector Analysis
No change
29
105
Principal Component Analysis
64.4 71.6
Proposed Wavelet based Method
82.4
Post Classification Change Detection The urban cover of the study area is extracted from the independently texture classified images and changes between the urban features are found using the post classification comparison change detection method. This is one of the most widely used methods of remote sensing change detection and it is based on pixel wise labelling of spectrally unique and identifiable land cover classes. The advantages of the method are no need in radiometric co-registration of images involved, low sensitivity to the spectral variations due to difference in the soil moisture and vegetation phenology, provision with “from-to” change information and quite high change detection accuracy.
Accuracy Assessment In order to assess the accuracy of the change detection procedures, the confusion matrix method is used. The columns of a confusion matrix contain the reference data collected through field visit and the rows represent the change map data. Confusion matrices compare, on a category-by-category basis, the relationship between known reference data ie ground truth (Tung Fung 1990 and Muller et al. 1998) and the corresponding results of change map. The performance of the proposed change detection method is evaluated using this confusion matrix for which about 250 ground truth features from the study area have been collected and used. From Table 5, the change map accuracy for the classical methods are 60.4%, 64.4% and 71.6% for the ID,
CVA and PCA respectively. But the highest accuracy about 82.4% is achieved for the proposed wavelet based change detection method.
Results & Discussion The performance of the proposed method is verified with an experiment. The images used for the experiment are of Madurai city as given in the study area. In this experiment, the texture features are extracted using Coiflet wavelet filters with three level of decomposition. The extracted texture features from three bands (bands 2, 3 and 4) were used to construct a 30x400x400 dimensional feature vector. The feature vector is then subjected to fuzzy c-means clustering for categorizing the images into urban, vegetation, wasteland, water body and hilly region, thereby obtaining the classified images of Madurai city of the periods 1996 and 2004. The Fig. 6 shows the urban distribution of the study area extracted from classified images and the urban growth map of Madurai city has been obtained using post classification comparison change detection algorithm. This map is subjected to accuracy assessment using the ground control data collected through field survey and the accuracy of the proposed method is compared with the classical change detection techniques like image differencing (ID), change vector analysis(CVA) and principal component comparison (PCC). The change maps obtained by these classical techniques are shown in Fig. 2. The quantitative results obtained by comparing the reference map (generated using ground control points) with the
Table 4 Error Matrix - Proposed Method Change Map Data Reference Data
Change
No change
Change
84
23
No change
21
122
Table 6 Urban Sprawl Area Theme Area of Change (in Pixels) Area of change (Sq.Km) Urban
3666
3.2994
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change detection maps yielded by applying the classical change detection techniques and the proposed technique are summarized in Tables 1, 2, 3 and 4. The image differencing technique resulted in a change detection accuracy of 60.4%. The accuracies obtained using change vector analysis and principal component comparison is 64.4% and 71.6% respectively. But the proposed technique gives an accuracy about 82.4% which is higher than the classical techniques. It is inferred from the change map that the northern region (Thiruppalai) of the Madurai city has expanded greatly over the 8 year period. The growth is also in north east (Anna nagar) and south west (Aarapalayam) areas of the city. As per the detail given in Tables 5 and 6, during the period 1996–2004, the Madurai city has grown about 3.29 sq. km.
Conclusion A novel technique for urban growth monitoring using multi-spectral satellite images has been developed based on wavelet transform. The results found demonstrate the effectiveness of wavelet frame based texture feature extraction method in urban change detection studies. This is because wavelet transform is used to represent the textural images in multiresolution domain. Since many textures in remotely sensed data have dominant frequency in middle frequency channels, the wavelets which are capable of focusing in the dominant frequency region provides a good space-frequency information which gives unique texture signature that leads to higher classification accuracy and thereby higher change detection accuracy. The study reveals that the Madurai city has grown dramatically in north, north east and south west areas which need much more attention while looking for infrastructure development. Over the 8 years period, the urban settlement has been increased to about 3.29 Sq.km. By considering the technological development taking place in the present era, the state government plans to construct a satellite town associated with tier II cities like Madurai, the second largest city of the state, Tamil Nadu. From the change map, it is suggested that the south, southeast and southwest sectors of the city where the sprawl rate is low can be used to set up satellite town. In the future, it is planned to use the high resolution satellite images to find the higher level land cover categories which may offer in-depth details on the
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type of land use changes and may be integrated with the socio-economic data to facilitate professional urban planning. Acknowledgements The authors are thankful to the Indian Space Research Organization(ISRO)- Bangalore, Department of Space, Government of India for providing financial assistance under RESPOND Scheme to carry out the research (Sanction Letter 10/4/506 dt. March 2005).
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