Hyperion Image Optimization in Coastal Waters - IEEE Xplore

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 51, NO. 2, FEBRUARY 2013

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Hyperion Image Optimization in Coastal Waters Yongchao Zhao, Ruiliang Pu, Susan S. Bell, Cynthia Meyer, Lesley P. Baggett, and Xiurui Geng

Abstract—Remote sensing of shallow waters may produce images characterized by limited image coverage, strong uneven background, and high noise/speckle levels, which contribute to the challenges of extracting spatial information. To better assess the submerged aquatic vegetation (SAV) habitat of coastal Pinellas County, Florida, USA, using Hyperion images, two operational image optimization algorithms, vertical radiance correction (VRadCor) for destripe and spectral recognition spatial smooth hyperspectral filter (SRSSHF) for denoise, were modified for use in the shallow coastal waters and then compared to other methods. The VRadCor compresses the cross-track radiance abnormity addressing both the along-track cambering effect with low frequency and the stripe effect with high frequency by estimating both the additive and the multiplicative correction factors. The experimental results show that VRadCor more effectively removes stripes from Hyperion images in comparison to other traditional algorithms. Application of SRSSHF, a special adaptive filter model that compresses the noise by using both spectral and spatial features, was effective for denoising for inner patch areas while retaining (or enhancing) subtle edges between different patches. The use of VRadCor and SRSSHF significantly improves the quality of images of coastal waters while retaining the spectral features of water/SAV. The optimization of the images may lead to improved feature classification or increased accuracy for parameter extraction. Index Terms—Denoise, destripe, seagrass, spectral analysis, spectral recognition spatial smooth hyperspectral filter (SRSSHF), submerged aquatic vegetation (SAV), vertical radiance correction (VRadCor).

I. I NTRODUCTION

I

N RECENT decades, new remote sensing systems have been developed to improve spectral and spatial resolution (e.g., the first spaceborne hyperspectral imager, Hyperion [30] and high-resolution sensor, IKONOS [12]). Despite poor signal features in aquatic environments due to strong absorption and backscattering characteristics of the water column, these sensors have been valuable for operationally mapping features in selected shallow-water coastal areas. For example,

Manuscript received August 21, 2011; revised December 9, 2011; accepted January 8, 2012. Date of publication August 16, 2012; date of current version January 17, 2013. This work was supported by the National Aeronautics and Space Administration ROSES-2008 Grant (Grant NNX09AT51G). Y. Zhao is with the Department of Geography, Environment, and Planning, University of South Florida, Tampa, FL 33620 USA, and also with the Key Laboratory of Technology in Geo-Spatial Information Process and Application Systems, Institute of Electronics, Chinese Academy of Sciences, Beijing 100191, China. R. Pu and C. Meyer are with the Department of Geography, Environment, and Planning, University of South Florida, Tampa, FL 33620 USA (e-mail: [email protected]). S. S. Bell and L. P. Baggett are with the Department of Integrative Biology, University of South Florida, Tampa, FL 33620 USA. X. Geng is with the Key Laboratory of Technology in Geo-Spatial Information Process and Application Systems, Institute of Electronics, Chinese Academy of Sciences, Beijing 100191, China. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2012.2205262

Kruse [13] utilized Hyperion imagery to map the detailed bottom composition of coral reefs in the Virgin Islands. Lee et al. [15] also used Hyperion imagery to assess the potential for mapping water properties and bottom bathymetry. Additional studies have employed hyperspectal imagery to estimate sea-floor reflectance and water depth (e.g., [16] and [31]), map distributions of a variety of coastal environments (e.g., [5] and [7]), estimate submerged vegetation biomass, and assess water quality (e.g., [3] and [35]). Two major themes emerge from a review of these remote sensing efforts: 1) Although the signal in the image of water area is relatively lower than that in a terrestrial area, and only a visible spectral range can be used, the information of water quality/depth and submerged components such as coral reefs, vegetation, and benthic substrate can be effectively extracted with a proper combination of image analysis algorithms; and 2) as pointed out previously (e.g., [13]), special image processing measures must be applied to compress non-target mixing signals which usually have similar signal intensities as targets. Remote sensing of shallow coastal areas is being utilized increasingly as an important source of information on seagrass distributions at a large spatial scale (e.g., [33]). Seagrasses, which are shallow water angiosperms, provide numerous ecosystem functions such as nutrient cycling, detritus production, sediment formation, and provision of habitat [2], [8]. Various methods have been developed for mapping and monitoring seagrass habitats using multi/hyperspectral optical images in many locations [1], [9], [11], [20], [21], [23], [27], [28]. The difficulties and countermeasures that accompany the use of imagery for seagrass mapping have been discussed broadly by Mount [19]. While difficulties caused by low signal condition, limited spectral range, strong signal mixing, and missing spectral information remain, optical remote sensing data are still the most useful data sources for our operational application. The image preprocessing strategy usually includes image optimization, both in spatial and spectral dimensions, to improve the data quality. In an ongoing study directed at mapping of seagrasses in coastal areas of Florida, USA, we found strong high-frequency along-track stripes and relatively higher noises compared to the low upward signal in Hyperion Level 1R (L1R) images in water areas, similar to issues mentioned by Kruse [13] and Tsai et al. [29]. The along-track stripes are not isolated and occurred at an interval of approximately 7–9 pixels. Unlike the randomly distributed and commonly isolated stripes, the Hyperion image presented a gradual change feature that was stronger close to the sides of the cross-track edge of the image or in images with short wavelengths (discussed below). The relatively higher noises compared to the low signal of the water area that we observed are also very distinct in Thematic Mapper (TM), Advanced Land Imager, and IKONOS images. Numerous examples of algorithms/approaches that can be used in preprocessing to denoise and/or destripe are currently

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available in the literature. However, after testing these methods (e.g., the adaptive filters for denoise as Lee, Enhanced Lee, Frost, Enhanced Frost, Gamma, Kuan, and Local sigma [25]; and the destripe methods as cross-track illumination correction (CTIC), histogram matching (HM), and the method introduced by Tsai et al. [29]), we could not effectively solve the special stripes and strong noises found in our remote sensing images from the shallow coastal waters. Most existing approaches were developed and validated to improve image quality for large terrestrial areas, rather than aquatic environments. For example, there are two algorithms applied in ENVI software [25] that can be used to assist for destriping: CTIC and the destripe function derived from solving the eight-element-sensor stripes of TM. However, CTIC is based on a polynomial simulation of the along-track statistics and thus can only be used for removing the low-frequency radiance bias. Therefore, theoretically it should not have an effect on high-frequency stripe removal. Our analysis using CTIC supported this assumption. The destripe function was actually designed to compress the along-track (horizontal) intervallic scan line striping, based on the normalization of each detector line to its respective mean. As such, this method is not suitable for Hyperion imagery. We also assessed or tested other destripe methods, such as traditional HM and the algorithm suggested by Tsai et al. [29]. HM, which performs well under conditions of an even background, was not ideal for our shallow water area due to its uneven background. Also, HM did not preserve the shape of the spectral curve, which is essential for analyzing hyperspectral images. Tsai’s method [29] is special for removing the isolated bad stripes, which also is not suitable for removing the highfrequency but consecutive stripes in our case. In addition, the stripes in our Hyperion L1R images were the residues after the regular relative calibration and destripe optimization. These residual stripes are relatively enhanced compared to the low signal of water area and cannot be adequately removed by the conventional methods. Therefore, an ideal destripe method should remove/compress both high- and low-frequency stripes in a limited, uneven area while retaining spectral features. Denoise addresses a traditional problem that detracts from image quality due to the presence of speckle-like noises which hamper the extracting of homogeneous areas and boundaries. In our case, the signal of the water body, including submerged aquatic vegetation (SAV), is very low compared to the noise level. The diffuse edges of SAV patches are strongly affected by the point spread function effect due to convolution blurring caused by the combination of water surface waves, perturbation of atmosphere, benthic substrate reflectance, and benthic reflectance related to the SAV and water column. The literature contains many algorithms based on a convolution filter (e.g., [10], [14], [17], [18], [36]) or other transform algorithms like wavelet (e.g., [6] and [24]) to address the noise level in images. Typical adaptive filters include the common functions in ENVI [25]. However, after assessing these filters for processing our imagery, we concluded that they were inadequate for our needs as these algorithms are intended to analyze terrestrial features and inadvertently lose the edge distinction among patches when complicated by the spectral properties of the water column. In an already blurred water image with a weak seagrass signal, such a loss strongly impacts the extraction of SAV information. An ideal method to retain edge distinction should adequately

Fig. 1. Location map of the two study areas, CLW/STJS and BCB, in Pinellas Country, Florida. Areas with submerged vegetation composed of seagrasses Thalassia testudinum, Syringodium filiforme, and Halodule wrightii and rhizophytic algae are indicated by the dark green shading.

use both spatial and spectral information. Accordingly, developing a method to remove the strong noise in a weak signal background for both spatial and spectral dimensions while retaining subtle edge information is a necessary step to improve both classification and assessment of SAV habitats. Therefore, the major objectives of this study include 1) developing two algorithms for destripe and denoise based on the evaluation of Hyperion image features and the analysis of signal composition in limited shallow water areas, both which will adequately utilize spectral information of the hyperspectral data, and 2) evaluating their optimization effectiveness of information extraction. The specific modifications of algorithms, including criteria of parameter estimation, are also tested and discussed. The overall goal is to improve the quality and efficiency of extracting SAV information from hyperspectral imagery. II. S TUDY A REAS Two study areas in Pinellas County, Florida, USA, were chosen: Clearwater Harbor/St. Joseph Sound (CLW/STJS, 28◦ 4 52 N, 82◦ 48 28 W) and Boca Ciega Bay (BCB, 27◦ 43 26 N, 82◦ 43 32 W) (Fig. 1). The areas have a low wave activity and significant tidal fluctuation. The water clarity is relative high with an absorption coefficient of about 0.8–2.1 m−1 . Depth ranges from approximately 0.5 m to 4.0 m in most of the study area with a mean depth of 1.5 m. The benthic substrate consists of a mixture of shell and sand particles or mixtures of sand and mud. The dominant seagrass species in the study area are Thalassia testudinum, Syringodium filiforme, and Halodule wrightii. Usually, the seagrass blades vary in length from about 10–30 cm and are dark green in color. The seagrass blades are commonly covered with varying amounts of gray to gray-brown epiphytes. Other SAV in the study areas include rhizophytic algae (green color), particularly at one site (STJS).

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Drift algae are also present. For the purposes of this study, seagrass, rhizophytic algae, and drift algae will be referred to as SAV. III. DATA AND Q UALITY A SSESSMENT Five scenes of Hyperion images acquired in 2009–2010 were selected for our study, two for CLW/STJS and three for BCB (Fig. 1). Hyperion has 220 unique spectral bands covering a spectral range from 0.4 to 2.5 μm with a bandwidth of about 10 nm [22]. Images were in L1R that already were radiance calibrated and preliminary geometrical corrected with a pixel size of about 30 m and digital number of 12 bits. Given that we generally focused on coastal water areas, the spectral range from 0.42–0.86 μm (band 8–51) were selected for the following analysis. An L1R image over a water area represents a signal combination of the water body (including all particulates), benthic substrate, SAV, water surface, atmospheric condition, imaging geometry, sensor system/calibration residua, and a mixed convolution blurring effect of all these factors. Thus, to extract the SAV signal effectively, the following factors related to image quality need to be assessed: • The random or near random noise/speckles caused by the thermal noise of sensor, the spatial variation of SAV coverage, the benthic substrate composition and smallscale (compared to the pixel size) relief, and the optical effect of a high-frequency part of wave. • The ambient variability information that is superimposed upon the heterogeneous distribution of SAV. This variation is a result of a water body with variable depth, current speed, clarity, and suspended matter, the low-frequency part of the wave, the thin cloud or massive water vapor, and the relatively large-scale change of seabed composition and relief. • The blurring effect caused by multiple factors, to which water surface waves greatly contribute. • The radiance bias caused by the sensor, which is a residual of preprocessing techniques applied to Hyperion images, such as relative calibration. Typically, preprocessing focuses on the terrestrial areas only, thus retaining a radiance bias in water areas in a strong contrast to its weak signal. In our case, it is apparent in the distinct stripes along the track (Figs. 2 and 3). Fig. 2 shows a part of a Hyperion image in the water area. The strong high-frequency along-track stripes on a strong uneven background are affected by small cloud/water blocks, wave speckles, and submerged materials. The left image was enhanced by an equalization method, while the right one is in normal 2% linear enhancement. Both images show a strong stripe level across the water area and can be easily observed even in a normal composite image. They also show a strong noise level which leads to a poor quality of water spectra. To display the along-track statistics of Hyperion and its cross-track profile of the high frequency, the upper gray image (Fig. 3) presents the X axis as the sample number crossing track and the Y axis as the band number (only bands 8–51 in VNIR region are shown), while the gray value represents the radiance value. The radiance value in the upper gray image is the statistical mean of the along-track expectation distribution with

Fig. 2. Hyperion images in the water area displaying a strong high frequency (interval of approximately 7–9 pixels) of along-track stripes on a strong uneven background. The images were composed by R = Band 32 (671 nm), G = Band 20 (549 nm), B = Band 11 (457 nm). The left one was enhanced by equalization by only referring to the displayed area (only water area), while the right one was enhanced by the 2% linear method by referring to the whole scene with terrain targets. Both show a strong stripe level in water area and can be observed even in normal composite image. A strong noise level is also present as indicated by “pepper and salt.”

Fig. 3. Along-track statistics of a Hyperion image (upper) and a cross-track curve of one band (lower). The cross-track curve shows peaks with an interval of approximately 8.5 pixels and an increase in stripe intensity at the edges (low and high sample number).

different cross-track samples and bands. The lower plot in Fig. 3 shows the cross-track curve of a blue band (457 nm) that has the strongest stripe phenomenon, which is important for analyzing remote sensing imagery for aquatic environments. The Y axis shows the radiance value, while the X axis is the same as the upper image. Both the image and plot show a strong highfrequency stripe (interval approximately 8.5 pixels), and within the interval, the change is gradual and continuous. Thus, these stripes in our study areas are quite different from traditional isolated stripes. The upper image also shows that the stripes increase in strength toward the both sides of the images, and as the wavelength shortens, the stripes increase in strength, too.

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TABLE I G ENERAL F EATURES OF THE H YPERION I MAGES U SED IN THE P RELIMINARY A SSESSMENT

The former phenomenon can also be seen in the lower plot. Our assessment for all five Hyperion images infers that the high-frequency stripes, along with strong noise, are common for Hyperion images and their stripe interval varies in a range of about 7–9 pixels (Table I). Our preliminary data assessment results are summarized in Table I. The S/N [4], [26], defined as the mean of the ratio of signal over noise for each spectral band, was estimated from homogeneous areas (flat field) manually selected in the water area. The signal is represented by the band mean of the flat field, while the noise is represented by the band standard deviation of the flat field. The S/N value shown in Table I is a mean of S/N values calculated from all VNIR bands in the wavelength range of 426–780 nm (bands 8–42); the “wave blur” was caused by several factors, mainly the surface wave, and it was qualitatively estimated by whether a benthic substrate can be clearly seen when compared to using high-resolution images of IKONOS or aerial photographs; the “wave block” and “cloud/vapor” were relatively large-scale (compared to the pixel size) uneven information superimposed on the submerged signal. The former had a feature of consecutive but directionally similar waves, whereas the latter had a feature isolated with an indistinct edge. The “uneven in open sea” represented an integrative assessment of the image feature in water where no signal of benthic substrate was recorded by the sensor and theoretically while there were no waves and clouds. The “stripes” were evaluated via the enhanced image over our study area. Accordingly, the qualitative features “stripes,” “wave blur,” “wave speckle,” “cloud/vapor,” and “uneven in open sea” as shown in Table I were evaluated visually due to the lack of clear criteria to quantify the parameters. The information in Table I clearly shows that our Hyperion images have an initial S/N estimation of about 120–150, a very strong feature of stripes along track and a strong superposition of non-submerged uneven signal, and the submerged signal may be strongly blurred by waves. These quality issues added a degree of difficulty to extract the weak SAV signal from the images and directed us to focus on the first two issues of denoise (to increase the nominal S/N ) and destripe (to compress the high-frequency along-track stripes). We also addressed the nonsubmerged uneven signal, mainly caused by the wave action and cloud/water vapor, as its existence makes the traditional destripe algorithms very difficult.

IV. M ETHODS AND A LGORITHMS A. Destripe Method: VRadCor In this paper, our method for destripe is a CTIC algorithm of vertical radiance correction (VRadCor) [36], [38]. VRadCor is a model to compress the cross-track radiance abnormity to assess both additive and multiplicative correction factors. It also addresses the along-track cambering effect with low frequency. It is based on the statistical spectral features of multispectral/hyperspectral images along the track, and is a special algorithm for relative calibration of sensors/images. We modified the algorithm to accommodate the water area with an uneven background. The stripe effect generally results from a combination of many causes such as the sensor’s response differentiation of multi-elements imaging system and the geometrical illumination changes with the angle of view. Some of these causes are additive while the others are multiplicative, and some of them are low frequency and others are high frequency. A universal destripe algorithm can be expressed as Ri (λj ) = GAINi (λj )Roi (λj ) + OFFSETi (λj )

(1)

where, Ri is the corrected image value of pixel sample i; Roi is its original value, and λj is the wavelength of band j; GAINi and OFFSETi are the multiplicative and additive linear correction factors for sample i, respectively. For an m sample (imaging elements) by n-band image, an ideal correction algorithm should determine a whole correction coefficient matrix with m ∗ n ∗ 2 elements as [[GAINij ], [OFFSETij ]], where i = 1, 2, . . . , m, j = 1, 2, . . . , n. However, in practice, it is difficult to obtain such a gainoffset matrix. Usually, with correction algorithms, by using image statistics such as HM and CTIC, one countermeasure is to decrease the dimensions by assuming that the statistics expectation (or distribution) crossing the track is even for each band, and thus obtain a one-spatial-dimension result for each band. Therefore, the obtained correction coefficient can only be a multiplicative one (gain) or an additive one (offset), but cannot be both. Unfortunately, in our case of using Hyperion images in shallow coastal water, the even precondition is false, and the single separately additive or multiplicative effect is incorrect as well.

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VRadCor provides a method to use both spatial and spectral information assuming that adjacent pixels with statistical expectation values along the track have similar spectra, which is a general inference of the correlation simulating analysis model (CSAM) for spectral analysis [38]. Thus, VRadCor may not adhere to the cross-track even precondition. The general algorithm of VRadCor is described as follows: 1) Select a multi/hyperspectral image Rmn , with a number of bands n ≥ 3 and a number of samples m ≥ 2 2) Select an area for the along-track statistics. The criterion of selection is to assure that the statistical expectation spectra represent similar mean objects. Invalid values in the image, such as overlapped (signal saturation) pixels, should be eliminated. 3) Calculate the mean from the selected part of image Rmn along the track for each band and record the cross-track spectral mean as Smn . To select the suitable part of the image for statistical analyses, a mask may be created and applied. 4) Smooth the Smn in the direction of the cross-track to remove/compress the information of real differentiation of adjacent pixels. If the differentiation is very small, based upon statistical values, this smoothing is not necessary. 5) Calculate the gain and offset factors, according to the assumption that the optimized statistic result, Smn , has similar spectral curves in adjacent pixels and the inference of CASM requires these curves to have a linear relationship, via the following expression: Si = GAINi ∗ S0 + OFFSETi

6)

7)

8) 9)

Fig. 4. Flowchart of the modified VRadCor method for destriping. The step in dashed frame with white background is the traditional algorithm. The smooth step in dashed frame with gray background is a selectable step.

(2)

where Si is the mean spectrum of sample i in Smn ; So is the optimized base spectrum to which the image will be corrected. VRadCor can now obtain both multiplicative and additive correction coefficients. Correct the different parts of cross-track radiance abnormity by selecting the different estimation methods for base spectrum S0 and by extracting the different frequency composition of Smn in the direction of crosstrack. VRadCor can be used to not only correct the lowfrequency component with a higher applicability than CTIC, but also correct the high-frequency stripes by behaving like a relative calibration method [38]. For example, S0 can be fixed to the mean of the whole image with an even expectation, while it represents the interpolation of adjacent pixels in an uneven area (i.e., our study areas). An iterative optimizing algorithm can also be operated to obtain the most optimized S0 estimation [38]. Select proper bands before the fitting to increase linear fitting effectiveness and to optimize correction coefficients. If necessary, the residual abnormity can be estimated by the common destripe algorithm. Obtain the correction coefficient matrix as [[GAINi ], [OFFSETij ]] or [[GAINiij ], [OFFSETi ]] at the end. Correct the image using the coefficient matrix.

These general steps described above were labeled out in the modified flowchart of Fig. 4.

B. Modification of VRadCor to Fit the Water Area Our modification of VRadCor is designed to accommodate the uneven background features, as described in Section III. At step 6 of the above processing flow, we choose the proper S0 and frequency composition. Given the general methodology of VRadCor that is typically utilized in terrestrial settings, it requires that statistics be calculated over a large area to assure that the along-track spectral expectations are almost the same in the direction of cross-track, and that the methods not be applied to an image with an uneven background, such as displayed by our images. Fig. 4 shows the general steps of our modified VRadCor which uses both spatial and spectral information, and allows the extraction of both additive and multiplicative correction factors over an uneven background compared to a weak objective signal. Following steps 5–6, (2) and the iteration circle in the flowchart Fig. 4, the processing result of VRadCor may be affected by both the spectral subset of linear fitting and the number of iterations used to optimize the uneven background. The modification includes: 1) In steps 4 and 6 above, using an interpolation of adjacent pixels in terms of the interval estimation as 7–9 pixels. 2) In iteration steps 5–7 above, iterating to optimize the additive coefficient, which is more important than the multiplicative coefficient as shown in Fig. 3. The correction coefficient matrix at the end is [[GAINi ], [OFFSETij ]],

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with m gain elements for all spectra in each sample, and m ∗ n offset elements for each band of each sample. 3) In step 6 above, estimating the proper frequency component based on the pixel interval knowledge and stripe feature described in Section III, by calculating the difference of a three-point convolution and an eight-point convolution. The three-point convolution is to smooth the spectra and remove the noise, while the eight-point convolution is to remove the stripes with a 7–9 pixel interval. Since the Hyperion L1R data were already geo registered, their along-track direction is actually oblique to northeast. Therefore, it was impossible to calculate directly the statistics in the direction of along track. Thus, a back-and-forth rotation was operated before and after VRadCor. The rotation angle, approximately 14◦ clockwise, as shown in Table I, was calculated with very high accuracy by identifying the geo-points. After the rotation, we found that the along-track stripe feature could be extracted very well because of the small distortion caused by the geometrical correction. Additionally, a simple water mask, created by a band threshold method via band 51 (864 nm) was applied to images to assure that the along-track statistic was based on the water area only. C. Denoise Method: SRSSHF Spectral recognition spatial smooth hyperspectral filter (SRSSHF) is a special adaptive filter model to compress or remove the noise/speckle in multi/hyperspectral images by using both spectral and spatial features of images [36], and is an algorithm also based on the spectral analysis model of CSAM [36], [37]. Considering that a general low-pass filter is R = R0 ⊗ K

(3)

where, R0 is the original speckled image, R is the speckleremoved result, K is the filter kernel, and ⊗ represents the convolution. SRSSHF needs an adaptive kernel as KS, so (3) is rewritten as R = R0 ⊗ (KS)

(4)

where, S is a judging function based on the interference of CSAM. Unlike other adaptive filters, which usually have an adaptive factor of sigma in the spatial dimension [14], the function S in SRSSHF is based on the spectral feature, which can improve the image quality in our study area. The first inference of CSAM [36], [37] provides a criterion to judge the similarity of pixels: If two spectral curves are completely similar, their correlation curve must be a line with a slope of 1 and an intercept of 0; otherwise, the two different reflectance curves must have different correlative curves. Another important inference is that CSAM can eliminate the common linear factors, both multiplicative and additive. Therefore, CSAM affords a method to not only remove both the multiplicative and additive random noises but also suppress other heterogeneous factors, albeit multiplicative or additive. Random thermal noise is not the only cause of speckled features in a flat area and some heterogeneous factors, such as the variability in the relative coverage of SAV but may also cause speckling. This speckle effect is caused by a small difference of

real objects among pixels. The CSAM affords another possibility to express the validity of pixel values that are determined by the shape features of spectral curves rather than by the statistic sigma parameter in spatial dimensions. The modified filter kernel in (4) can be rewritten as Kk,l =KS     k1,1 k1,2 . . . k1,n   s1,1 s1,2 . . . s1,n      k2,1 k2,2 . . . k2,n   s2,1 s2,2 . . . s2,n     =  . . . . . . .. . . . ..   .. .. . . . ..   ..    km,1 km,2 . . . km,n sm,1 sm,2 . . . sm,n k,l    k1,1 s1,1 k1,2 s1,2 . . . k1,n s1,n     k2,1 s2,1 k2,2 s2,2 . . . k2,n s2,n   =  (5) .. .. .. ..  . . . .     km,1 sm,1 km,2 sm,2 . . . km,n sm,n k,l where Kk,l is a modified filter, K is a typical low-pass filter kernel which can be considered as a distance weighted matrix and is constant without subscript (k, l), Sk,l is a logic relation matrix for pixel (k, l) determined by CSAM, and m, n are the size of the kernel. The subscript (k, l) means that the Kk,l and Sk,l , unlike K, vary with different pixels. Hence, SRSSHF is considered as an adaptive filter. The elements of Sk,l are defined as   si,j;k,l = fL pk−i− n2 ,l−j− m2 , pk,l , i = 1, 2, . . . , n; j = 1, 2, . . . , m

(6)

where Si,j;k,l represents the elements (i, j) of Sk,l , p is the spectral vector of each pixel. Function fL (p1 , p2 ) is a logic judging function. If the two spectral vectors p1 , p2 are the same according to the aforementioned two inference criteria of CSAM, then the function will be 1, otherwise, it will be 0. Therefore, Sk,l is a logic value matrix with ⎧ 1 If CSAM says Yes, two pixels (k-i-n/2, l-j-m/2) ⎪ ⎨ and (k, l) are in same spectral property si,j;k,l = ⎪ ⎩ 0 If CSAM says no, two pixels (k-i-n/2, l-j-m/2) and (k, l) are in different spectral property. (7) And the SRSSHF filter can be expressed as m Rk,l = R0 ΘKk,l

=

n m i=0 j=1

· si,j;k,l .

Rk−i−n/2, l−j−m/2 · ki,j · si,j;k,l



n m

ki,j

i=0 j=1

(8)

Compared to (3), SRSSHF is intellectualized potentially. SRSSHF insures that only the pixels with similar spectral properties around the center pixel are calculated into the average for smoothing. The general feature of this filter is recognizing the target in the spectral dimension while smoothing in the spatial dimensions. SRSSHF has many benefits: Removing the noise in an homogeneous area while retaining the boundary without blurring; keeping small objects from being erased; automatically smoothing along the direction of linear objects like navigational

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Fig. 7. Cross-track distribution of the mean statistics calculated across the track from band 8. The thin curve represents values prior to applying VRadCor (thick curve). The plot shows that stripes with an interval of approximately 8 pixels were mostly removed from the uneven background.

Fig. 5. Flowchart of SRSSHF used to denoise an image. (ROI = Region of Interest). Fig. 8. Comparison of the spectral curve (reflectance with a scale of 10 000 after FLAASH) before (thin) and after (thick) applying VRadCor for a randomly selected pixel. The VRadCor was applied on the L1B radiance data then followed by FLASSH in order to compare with our experiential knowledge on reflectance.

the patch boundary and small objects. Therefore, SRSSHF is an appropriate filter for our image features.

D. Modification of SRSSHF to Fit the Water Area

Fig. 6. Comparison of a subsection of our study area before (left) and after (right) VRadCor processing. These results were produced after equalization enhancement. Note that the stripes after processing were significantly reduced while the uneven (heterogeneous) phenomena in patches were still retained. The figure was produced with Hyperion image acquired on 2009-10-8.

channels; identifying the isolated error data points; insensitivity to the kernel size; and lacking an edge effect, which is very strong in other low-pass filters. Another important benefit of SRSSHF is the ability for multiple iterations to be conducted with the de-noise effect increasing continuously while retaining

Our modification of SRSSHF focused on setting the value of judging criterion to adapt for use in aquatic environments. The general SRSSHF criterion values are set for common land objects and water features are considered as a flat patch and smoothed as a whole. In this paper, the details of SAV habitats remain within a background of higher noise as compared to the weak SAV signal. After testing the judging function, we found a correlation coefficient of about 0.994 and a slope bias of about 0.06 were proper criteria values for all of our five Hyperion L1R images. A kernel size of 7 was selected according to the result of VRadCor interval of 7–9 pixels (Fig. 3). The maximum number of iterations was set to 20. Fig. 5 presents a general flowchart of our application of SRSSHF. The input image was processed by VRadCor, and the spectral subset of judging function included bands 8–51.

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Fig. 9. Denoise effect in spatial dimension of SRSSHF. (Left) Raw data before applying SRSSHF and (right) after applying SRSSHF. (Top) Image without zoom. (Bottom) Image zoomed in and enhanced to show the detail (as in the frame of upper images). The figure was produced with a Hyperion image acquired on 2009-10-8.

E. General Image Optimization Steps Accordingly, our optimization method reported in this paper includes following steps: 1) Assess images with some image feature statistics, e.g., the stripe interval and noise level of the images. 2) Calculate the geo-points and rotation angle to geometrically retrieve the images. 3) Apply VRadCor and optimize its results, and compare the results created with other destripe methods (HM, CTIC and the destripe function in ENVI system). 4) Apply SRSSHF and optimize its results, and compare the results created with other adaptive filters (Lee, enhanced Lee, Forest, enhanced Forest, Gamma, Kuan, and local sigma in ENVI system). 5) Conduct atmospheric correction by FLAASH in ENVI system, for all images before VRadCor, after VRadCor and after SRSSHF. 6) Check and compare the images at the level of reflectance.

V. R ESULTS AND A NALYSIS A. VRadCor We applied VRadCor directly to the five images in radiance to remove strips. Then, a comparative analysis of results between before and after running VRadCor was conducted based on the images in reflectance after atmosphere correction with FLAASH in ENVI system. Fig. 6 presents a part of Hyperion image in the water area before (left) and after (right) running VRadCor. The images were enhanced using an equalization method over a fine-scale water area. The processing result shows that, after running VRadCor, even with a strong enhancement, the stripes with an interval of about 8 pixels and varying with the distance to center and wavelength were difficult to detect visually. Also, the uneven information in the water area was enhanced significantly. After running VRadCor, both the uneven background and the spectral feature of objects were mostly retained. Interestingly, the distracting line near the center (Fig. 6) was almost completely removed, demonstrating

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Fig. 10. Comparison via an arbitrary profile between the images before (thin) and after (thick) 20 iterations of SRSSHF. The arbitrary profile was obtained from band 8. The images were also processed with FLAASH, so the data value is in reflectance with a scale of 10 000.

Fig. 12. Comparison of edge detection before (left) and after (right) applying SRSSHF. The portion in the frame and the track of channels show the detailed features enhanced by SRSSHF. The figure was produced with Hyperion image acquired on 2009-10-8.

Fig. 11. Comparison of the spectral features of a randomly selected pixel without SRSSHF(raw data), after 20 iterations of SRSSHF, and after 5 iterations of SRSSHF.

that VRadCor can be used to extract the high-frequency composition information over an uneven background. Moreover, the spectral curve shape was retained after processing with VRadCor, although the pixel value was significantly altered after the atmospheric correction (Figs. 7 and 8). B. SRSSHF After applying VRadCor to the five images, they were processed using SRSSHF to remove noise over the water area using three iteration schemes (iterating 1, 5, and 20 times). After running the two algorithms (VRadCor and SRSSHR), the images were further processed with atmospheric correction by FLAASH in order to calibrate the images in reflectance. Figs. 9–12 show the denoise effects both in the spatial and spectral dimensions. The corresponding nominal S/N s were also calculated and shown in Table II. SRSSHF significantly removed the noise in patches while relatively enhancing the subtle boundaries among patches without blurring. The small size targets/patches were retained clearly on both zoomed-in and zoomed-out images (Figs. 9 and 10). In the flat area, the inner noise was almost completely removed across pixel samples 130–200, and in the small area across pixel samples 100–130, and the patch edges were retained and relatively enhanced. Similarly, both patch edges of the flat area at samples 130 and 200 were clearly sharpened, and a small target at pixel

163 was clearly retained even after 20 iterations. These results indicate that the SRSSHF with 20 iterations is appropriate to effectively denoise. SRSSHF not only improves the data quality in spatial dimensions, but also remarkably improves the data quality in the spectral dimension. Fig. 11 demonstrates the effectiveness of image processing with SRSSHF by using a spectral curve from a randomly selected pixel. A similar conclusion can be drawn from the edge detection/sharpening result shown in Fig. 12 as the application of SRSSHF provides distinct edge information, particularly in the frame area. Nominal S/N estimation from selected flat water areas indicated that images processed with the VRadCor destripe technique did not improve the S/N of images. The ratio of S/N was calculated from the S/N after optimization divided by the S/N of raw data. The results suggest that with an increasing number of iterations, the nominal S/N increases about 30× from approximately 140 up to 4500.

VI. D ISCUSSION In this paper, traditional destripe algorithms were tested with our Hyperion data under the same condition as for testing the modified VRadCor, and we found that they did not work properly with our data. The other traditional destripe algorithms applied to our images included HM, CTIC, and the destripe function in the ENVI system. Table III shows the capability comparison among four typical destripe methods. We noted that Tsai’s method [29] is special for removing isolated bad stripes, and some features are not available for comparison. In general, we found that other methods did not produce better results than our modified methods. For example, the destripe function in ENVI provided poor results that were strongly affected by the uneven background of the water area. A common problem for the traditional destripe algorithms was the inability to adequately separate the high-frequency stripes over

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TABLE II E STIMATION AND C OMPARISON OF R ATIO OF S IGNAL TO N OISE (S/N ) A FTER I MAGE O PTIMIZATION P ROCESSING

TABLE III C APABILITIES OF D IFFERENT D ESTRIPE M ETHODS

the uneven background, and the unacceptable superposition of an additional strong low-frequency signal. In addition, the HM method and common destripe function did not preserve the spectral features and compress the whole image into a same expectation value and the CTIC did not effectively remove the high-frequency stripes necessary for the study sites. Two major conclusions can be gleaned from these comparisons: 1) Modification of the VRadCor is essential for separating the low-frequency uneven background from the high-frequency stripe signal and for fitting the high spatial unevenness of the shallow water area while accommodating the low and mixed signals; and 2) only the previous and modified VRadCor preserve the spectral features while solving the stripe problem in spatial dimension by using spectral information. Thus, we suggest that our modification is a useful process in coastal settings with image conditions similar to ours. When we compared the results of different algorithms for destripe, we found that the performance of each algorithm varied with stripe type. Based upon assessment results (Table I), the Hyperion images (Figs. 2, 3, and 6) over the shallow water area have a high frequency of stripes with a sample interval of approximately 7–9 pixels. The stripes were not the same as those generally caused by the sensor elements, which usually are isolated and randomly distributed. We suggest that these stripes were not caused by any sensor responding differently, but by the residue of Hyperion image preprocessing, which is based on the image features of terrestrial area. Consequently, in water areas specifically, the stripes become stronger as the spectral wavelength shortens and the distance to the edge decreases. Therefore, it is reasonable that the stripes are distinct only over the water area. This provides an explanation for the inadequacy of the traditional destripe algorithms, including earlier versions of VRadCor.

TABLE IV C APABILITY C OMPARISON FOR D IFFERENT A DAPTIVE F ILTERS

Theoretically, the modified VRadCor method can identify both the additive and multiplicative correction factors via fully using the spectral information. In our case, the correction factor to remove the stripes in the Hyperion images was mostly additive. While results for destriping are encouraging, some residual stripes remained after the modified VRadCor (see the thick curve in Fig. 7), which is a shortcoming of our image processing method. This may be caused by a statistical bias resulting from optimizing the best uneven background at step 6 in Section IV-A. However, even so, the residue level was relatively low (Fig. 6). In this paper, we also conducted a comparative analysis between SRSSHF and other adaptive filters for denoise for all Hyperion images. Table IV presents the capability comparison of SRSSHF with other adaptive filters. From Table IV, it is apparent that SRSSHF and its modification outperform all other adaptive filters. Therefore, based on our SRSSHF test results for all Hyperion images and other multispectral images such

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as Landsat TM, we are confident that SRSSHF and its modification can be applied to other coastal shallow areas for any image processing with > 3 bands and > 2 pixels. In addition, the preservation of some linear features due to the directional feature of SRSSHF is beneficial in some cases after denoise and is not available for other adaptive filters. For example, in our study areas, the navigation channel features were effectively retained after the 20 iterations of SRSSHF, as shown in Figs. 6 (upper) and 12. Although, as noted in the introduction, it is difficult for the algorithms in general to distinguish among different noise/speckle sources, our SRSSHF can handle noise that is a random or near random noise caused by the thermal noise of sensor, uncertain spatial change of ground objects, such as SAV coverage, benthic substrate and benthic fine-scale (compared to the pixel size) relief, and the uncertain optical effect of the high-frequency waves. As a result, after applying SRSSHF, particularly with larger number of iterations, the inner variations in each patch, both the noise and the small-scale variation among pixels were removed. It may be expected that denoised images should improve classification and assessment of SAV resources. For SRSSHF, both the number of iterations and judging parameters greatly influenced the results. As restriction of judgment criterion was increased, the amount of detailed information preserved also increased. Moreover, as the number of iterations increased, much more smooth patches were produced. Therefore, the processing effectiveness can be controlled depending upon the users’ application. VII. S UMMARY AND C ONCLUSION In this paper, to increase the reliability and accuracy of extracting the SAV signal in the shallow coastal water areas, we introduced, modified, and tested two special optimization algorithms for destripe (VRadCor) and denoise (SRSSHF). The modifications were used to solve the qualitative problems found in a preliminary assessment of the quality of Hyperion images, such as strong stripes and noises as compared to the weak upward signal of water area. After comparing other traditional methods, the modified VRadCor and SRSSHF optimization algorithms were applied to five images and indicated improvement in the image ratio of signal to noise and in image visualization. VRadCor was used for processing the uneven background information to separate stripe information from the base spectrum, while SRSSHF was used to estimate the judging criteria for distinguishing the fuzzy submerged edge in water area with weak signal while with strong uneven background. These algorithms can be used to improve image quality in both spectral and spatial dimensions while retaining general spatial features such as edges and channels, and spectral features such as the shape of spectral curve. ACKNOWLEDGMENT The authors greatly appreciate the assistance of Pinellas County Department of Environment Management in collecting field data. Special thanks to Ms. K. H. Levy and Ms. M. Harrison, Pinellas County Department of Environment Management, FL, and Mr. D. English, College of Marine Science, University of South Florida, St. Petersburg, FL. The images were provided by NASA/USGS.

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Yongchao Zhao received the Ph.D. degree in geology from Peking University, Beijing, China, in 1999. Currently, he is an Associate Professor in the Institute of Electronics, Chinese Academy of Sciences, Beijing. His research interests include hyperspectral image precise processing-calibration, preprocessing, and optimization; spectral characteristic of typical terrestrial objects; integrative apparent spectral models of these typical objects; and information extraction from hyperspectral images databased on the precise understanding on spectral characteristics.

Ruiliang Pu received the Ph.D. degree in cartography and geographic information systems at the Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, from the Chinese Academy of Sciences, Beijing, China, in 2000. Currently, he is an Associate Professor with the Department of Geography, Environment, and Planning, University of South Florida, Tampa. His current research interests are in mapping and characterizing seagrass habitats using spacecraft observations, urban environmental studies, and urban tree canopy mapping/species identification using thermal and high-resolution satellite imagery.

Susan S. Bell received the Ph.D. degree in marine science from the University of South Carolina, Columbia, in 1979. Currently, she is a Professor in the Department of Integrative Biology, University of South Florida, Tampa. Her research interests include marine ecology, landscape ecology of coastal habitats, and restoration ecology.

Cynthia Meyer is currently working toward the Ph.D. degree in the Department of Geography, Environment, and Planning at the University of South Florida, Tampa. Expanding from her Master’s thesis, her research involves applying remote sensing and spatial analyses to assess the impacts of natural and anthropogenic stressors on the marine ecosystem. In addition, she works for the NOAA Fisheries Service Sustainable Fisheries Division developing management strategies for the Gulf of Mexico marine resources. Future research applications will include moving toward ecosystembased management strategies and extending the application of remote sensing to the marine environment.

Lesley P. Baggett received the Ph.D. degree in marine science from the University of South Alabama, Mobile, in 2010. She was a Postdoctoral Associate at the University of South Florida, Tampa, from 2010 to 2011 and is currently a Postdoctoral Associate at the Dauphin Island Sea Laboratory, Dauphin Island, AL. Her research interests include anthropogenic effects on the benthic ecology of nearshore marine habitats such as seagrass beds and oyster reefs, as well as the conservation and restoration of these habitats.

Xiurui Geng received the Ph.D. degree in hyperspectral remote sensing from the Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China, in 2005. Currently, he is an Associate Professor in the Key Laboratory of Technology in Geo-spatial Information Process and Application Systems, Institute of Electronics, Chinese Academy of Sciences. His research interests are hyperspectral data understanding and algorithm development; understanding, applying, and developing mathematical methods via algebra, geometry, analysis, and statistics viewpoints, respectively, for hyperspectral data; and target detection, feature extraction, change detection, and mixed pixel analysis.