Mapping urban impervious surface with dual-polarimetric SAR data: An improved method

Landscape and Urban Planning 151 (2016) 55–63

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Research paper

Mapping urban impervious surface with dual-polarimetric SAR data: An improved method Hongsheng Zhang a,b , Hui Lin a,b,c,∗ , Yu Li a , Yuanzhi Zhang d , Chaoyang Fang e a

Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong d National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China e Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Jiangxi 330022, China b c

h i g h l i g h t s • • • •

Dual-polarimetric SAR data were exploited for impervious surfaces mapping. Usage of polarimetric SAR data was superior over single polarization data. Some individual polarimetric features were found to provide negative effect. Approximately 3.5% improvement was achieved using all polarimetric features.

a r t i c l e

i n f o

Article history: Received 12 May 2015 Received in revised form 10 March 2016 Accepted 12 March 2016 Keywords: Impervious surfaces PolSAR Optical and SAR data fusion Alpha-H decomposition

a b s t r a c t Synthetic aperture radar (SAR) data can provide complementary information to improve the mapping of urban impervious surfaces. However, most studies have focused on using only single polarization SAR data. This paper presents a comparative study on the combined use of multispectral optical data and dual polarization SAR data to identify urban impervious surfaces. The experimental results using SPOT5, TerraSAR-X and ALOS PALSAR data were consistent compared with our previous results using single polarization SAR data. The two-fold result showed that polarimetric SAR images were generally superior to single polarization SAR data for extracting impervious surface areas, although not every individual polarimetric feature could provide a positive result for impervious surfaces mapping. Compared with using only optical and SAR data, the separate HH and HV polarization data improved the accuracy of the results. The incorporation of both Entropy and Alpha features also improved the accuracy. However, the HH/HV ratio and the separate use of coherence did not provide positive results. Noticeably, a combination of all of the dual-polarimetric SAR features was capable of obtaining the best accuracy, with an improvement of approximately 3.5% compared with that of only using SPOT-5 images. This result indicates the superiority of dual-polarimetric SAR data over single polarimetric SAR data for the mapping of urban impervious surfaces. © 2016 Elsevier B.V. All rights reserved.

1. Introduction The mapping of impervious surface areas and spatial distributions is important for the environmental study of urbanized areas (Ma et al., 2014; Wu & Thompson, 2013). Among all approaches

∗ Corresponding author at: Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. E-mail addresses: [email protected] (H. Zhang), [email protected] (H. Lin), [email protected] (Y. Li), [email protected] (Y. Zhang), [email protected] (C. Fang). http://dx.doi.org/10.1016/j.landurbplan.2016.03.009 0169-2046/© 2016 Elsevier B.V. All rights reserved.

to impervious surfaces mapping, remote sensing is becoming the major technique due to its convenience and low cost from local to global scales. Many methods have been proposed to extract impervious surfaces using optical remote sensing data (Deng & Wu, 2013; Hu & Weng, 2009, 2011a, 2011b; Van de Voorde, Jacquet, & Canters, 2011; Weng, 2012; Weng & Hu, 2008; Wu & Murray, 2003; Yang & Li, 2013; Yang, Huang, Homer, Wylie, & Coan, 2003; Zhang, Zhang, & Lin, 2014). More recently, other data sources were evaluated and applied to estimate impervious surfaces, such as time-series planimetric data and nighttime light data (Ma et al., 2014; Wu & Thompson, 2013). However, accurate impervious surfaces mapping is still challenging because of the diverse urban land cover classes

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and the relative complexity of climatology and phenology effects (Deng & Wu, 2013; Weng, 2012; Zhang et al., 2014). For example, separating impervious surfaces from non-impervious surfaces is difficult when their spectral signatures are similar. Moreover, shaded areas from tall buildings and trees in urban areas are often confused with dark impervious surfaces. While various data sources have been evaluated to improve impervious surfaces mapping, the potential of Synthetic Aperture Radar (SAR) data was much less investigated. Because of the sensitivity to surface geometric properties (e.g., roughness), SAR data can compensate optical data to accurately identify urban areas (Calabresi, 1996; Henderson & Xia, 1997; Stasolla & Gamba, 2008; Zhang, Zhang, & Lin, 2012; Zhang et al., 2014). Many approaches have been proposed to extract urban areas from SAR data. A Markovian classification algorithm was developed to classify urban cover from high-resolution SAR images (Tison, Nicolas, Tupin, & Maitre, 2004). Texture has been widely identified as a beneficial factor for urban classification using SAR data (Dekker, 2003; Gamba & Aldrighi, 2012; Majd, Simonetto, & Polidori, 2012; Voisin, Krylov, Moser, Serpico, & Zerubia, 2013). Multi-temporal SAR data were also employed to improve the extraction of urban areas (Hu & Ban, 2012; Niu & Ban, 2013). Nevertheless, most of these studies considered only SAR data with single polarization. Multi-polarimetric SAR data offer a much better capacity for distinguishing different scattering mechanisms of ground targets; hence, they were applied to urban area extraction in recent years. Touzi decomposition was applied to polarimetric SAR data for extracting urban areas (Bhattacharya & Touzi, 2011). The RR-LL (R-right; L-left) circular-polarization correlation coefficients were developed to detect man-made targets from SAR data in urban areas (Ainsworth, Schuler, & Lee, 2008). Fully polarimetric (FP) features were also derived from Radarsat-2 FP SAR data for urban studies (Guo, Yang, Sun, Li, & Wang, 2014). All of these above-mentioned studies highlight the advantages and necessities of using polarimetric SAR to improve the extraction of urban areas. The FP SAR system transmits both horizontal and vertical polarized signals alternatively and receives them coherently to measure the polarimetric characteristics of the ground target. Based on this information, the scattering matrix of any combination of transmitted and received polarizations can be derived. However, this approach suffers from the disadvantages of system complexity and reduced swath width caused by doubled pulse repetition frequency (Chen & Quegan, 2011). Therefore, the dual polarimetric (DP) SAR mode is often used as a way to balance swath width and polarization capability (Souyris & Mingot, 2002; Souyris, Imbo, Fjortoft, Mingot, & Lee, 2005). Additionally, SAR data were used jointly with optical data to classify urban areas and obtained promising results (Cao & Jin, 2007; Gamba & Houshmand, 2001; Zhang et al., 2014). The results of these studies indicate that a joint use of optical and SAR data in urban land cover classification yields higher quality than those when they are used separately. However, the potential of SAR data should be further explored for impervious surfaces mapping. Although dark impervious surfaces (e.g., old rooftops, asphalt roads or parking lots) and bright impervious surfaces (e.g., new concrete roads, new rooftops or concrete parking lots) have similar backscattering characteristics in SAR images, they can have different behaviors in optical images because of their differences in spectral reflectance. Therefore, by incorporating SAR data with optical data, dark impervious surfaces and bright impervious surfaces can be better separated. While most of the existing studies that combine optical and SAR data employ only single polarization SAR data (Cao & Jin, 2007; Gamba & Houshmand, 2001; Zhang et al., 2014), the potential of combining polarimetric SAR data with optical data for impervious surface estimations is still under-explored. This study aimed at evaluating different features of polarimetric SAR data to improve

the accuracy of urban impervious surface estimations. In this study, Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data, with HH (Horizontal transmitting, Horizontal receiving) and HV (Horizontal transmitting, Vertical receiving) polarizations, from the area along the boundary of Shenzhen and Hong Kong were used. Moreover, to evaluate the complementary information carried by dual-polarimetric SAR data, TerraSAR-X data with single polarization and Système Pour l’Observation de la Terre 5 (SPOT-5) data over the same area were also employed for comparison. 2. Study area and data sets The Pearl River Delta (PRD) in Southeast China was selected as the study region (Fig. 1). During the past three decades, the PRD has been dramatically urbanized, consequently leading to severe environmental pollutions. There is an urgent demand in continuously monitoring the dynamic urbanization process in PRD, while satellite remote sensing provides a cost-effective approach. Nevertheless, the PRD lies in a rainy and cloudy area with large amounts of cloud contamination, making it challenging for urban monitoring with optical remote sensing data. In this analysis, a study site located on the boundary of Shenzhen and Hong Kong, next to the Pearl River Estuary (PRE), was selected (Fig. 1). This study employed a SPOT-5 data (obtained on November 21, 2008) at a 10-m resolution, a TerraSAR-X data (backscatter data obtained on November 16, 2008) at approximately a 3-m resolution and an ALOS PALSAR data (backscatter image obtained on November 11, 2008) at approximately a 16-m resolution (Fig. 1). The TerraSAR-X data was single polarized, and the PALSAR data were dual polarized data with HH and HV polarizations. The SAR data were preprocessed with different strategies for the TerraSARX data and dual polarization PALSAR data. First, for the TerraSAR-X and PALSAR data, the Enhanced Lee filter (Lee et al., 1999; Lopes, Touzi, & Nezry, 1990) was applied to reduce speckle noise at a resolution of 3 m. Second, for the PALSAR data, because the extraction of polarimetric features (also referred to as the polarimetric parameters; we are using the term features instead of parameters following the concept of feature extraction in computer science) requires the phase information from the Single-look complex (SLC) product, the SLC data were processed and analyzed by the methods introduced in Section 3.1 of this paper to extract these features. Then, a multi-look was applied to both the original PALSAR data and the extracted polarimetric features. Finally, both the TerraSAR-X and PALSAR data, including the extracted polarimetric features, were co-registered at a 10-m resolution with 20 ground control points. The co-registration error (root mean square error) was below 0.5 pixels. 3. Methods 3.1. Feature extraction Various features were extracted from both the dualpolarimetric SAR data and the optical data. The extracted features consist of two categories: polarimetric features from dual-polarimetric SAR data and textural features from single polarization SAR and optical data. First, the polarimetric features were extracted, including the HH/HV ratio, the Alpha and the Entropy decomposition parameters, and the coherence between HH and HV channels. These polarimetric features have been widely reported to reflect the scattering mechanisms of targets, as found through the analysis on the intensity/power ratio, phase and correlation information of polarimetric SAR data (Haralick, Shanmuga, & Dinstein, 1973; Marceau, Howarth, Dubois, & Gratton, 1990;

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Fig. 1. Location and satellite images of the study area, (a) location of the study site, (b) false color composite image of SPOT-5 data, RGB (Red-Green-Blue) bands corresponding to the short-wave infrared (SWIR), green and red bands, (c) TerraSAR-X image, and (d) false color composite image of ALOS PALSAR data, RGB bands corresponding to the intensity of HH, HH and HV polarizations data. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Puissant, Hirsch, & Weber, 2005). For example, the Alpha-Entropy decomposition can characterize various scattering mechanisms, including single bounce, double bounce and volume scattering, providing useful information for urban land cover classification. Technically, the HH/HV ratio was based on the backscattering power of the two polarization data (Eq. (1)), while Alpha and Entropy were calculated by analyzing the eigenvalue and eigenvector of the coherency matrix of the dual-polarimetric SAR data (Marceau et al., 1990; Puissant et al., 2005). The Alpha and Entropy (H) values can be calculated using Eqs. (2) and (3). The coherence between the HH and HV complex backscattering coefficients was calculated using Eq. (4). For, SHH and SHV are the backscattering coefficients of HH and HV channels respectively. P1 and P2 were calculated from the eigenvalues of the coherency matrix, and ˛ was calculated from the eigenvector of the coherency matrix. SHH SHH ∗  R= SHV SHV ∗ 

(1)  2

Alpha = ˛ (P1 − P2 ) + P2 H = P1 log2 P1 + P2 log2 P2



Coh =

|SHH SHV ∗ | SHH SHH ∗ SHV SHV ∗ 

(2) (3)

2

(4)

Second, texture features were also calculated for the optical and single polarization SAR images. The popular grey level cooccurrence matrix (GLCM) approach (Haralick et al., 1973) was employed to analyze the texture features of the SPOT-5 image. GLCM has been widely applied to many remote sensing applications to comprehensively extract texture information in an image, such as the compactness and granularity. However, the successful application of GLCM is not easy and depends on several impor-

tant factors. For example, the block size (the moving window size) when scanning the image and the texture measurements that are calculated based on the GLCM have been a major issue (Marceau et al., 1990). In this study, regarding remote sensing classification in urban areas, it was reported that a window of 7 × 7 pixels is suitable, with a test on the resolutions from 2.5 m × 2.5 m to 10 m × 10 m (Puissant et al., 2005). Moreover, four texture measures, the homogeneity (HOM), dissimilarity (DISS), entropy (ENT), and angular second moment (ASM), were identified as effective indicators for the texture description of different urban land cover types (Puissant et al., 2005). 3.2. Optical and PolSAR data fusion Different combinations of features were generated and tested to examine their contributions to impervious surface estimations. Two strategies were employed to combine various features. First, Table 1 shows the various combined features of optical and SAR data that were used to investigate the potential of dual polarimetric features to map impervious surfaces compared to optical data and single polarization SAR data. Second, to evaluate different polarimetric features in terms of their contributions to impervious surfaces estimations, the polarimetric features extracted from dual polarimetric ALOS data were tested individually, namely, the HH and HV backscattering coefficients, the HH/HV ratio, the Alpha and Entropy decomposition values (Cloude & Pottier, 1996, 1997) and the coherence between HH and HV data. Various combinations of features were then used for the impervious surface estimation. The fusion process was incorporated into the classification procedure by using various classifiers. Generally, both the optical and SAR features were simply combined to generate a longer vector for each pixel. Then, different classifiers

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Table 1 Different combinations of optical and SAR features. Combinations Code

Optical and SAR data to be combined

SPOT Only ALOS Only ALOS(Pol) TSX Only SPOT + ALOS SPOT + ALOS(Pol) SPOT + TSX SPOT + ALOS + TSX SPOT + ALOS(Pol) + TSX

Only SPOT data and its texture features Only ALOS data and its texture features ALOS data with texture features and polarimetric features Only TerraSAR-X data and its texture features SPOT data and ALOS data and their texture features SPOT data and its texture features, plus ALOS data and its texture and polarimetric features SPOT data and TerraSAR-X data and their texture features SPOT data, ALOS data and TerraSAR-X data and their texture features SPOT data, ALOS data and TerraSAR-X data and their texture features, plus the polarimetric features of ALOS data

performed different analyses over these combined features. For example, the maximum likelihood classifier (MLC) fused the two data sets by modeling the joint probability distribution, which might be different from that using only a single data set. This study employed the Support Vector Machine (SVM) to fuse the two data sources to classify impervious surfaces and non-impervious surfaces using various combinations of optical and SAR features. A successful SVM depends on several important factors (Vapnik, 1998). One factor is the selection of kernel functions for mapping the data into high dimensional space when the original data are not linearly separable. Another important factor is the strategy for classifying multiple classes because the basic SVM is designed for binary classification. Moreover, the optimization of parameters in the selected kernel function is also necessary for the SVM results. For the kernel function, this study employed the Radial Basis Function (RBF). Additionally, the value of the penalty (C) and Gamma (G) parameters in RBF were optimized by a cross-validation procedure (Zhang et al., 2012). For the multi-class classification, we adopted the one-against-rest strategy (Weston & Watkins, 1999).

3.3. Classification scheme and validation Finally, the accuracy of impervious surfaces mapping was evaluated by calculating the confusion matrix, thus evaluating the potential of dual-polarimetric SAR data. For training samples, 1986 pixels were collected, and 2635 pixels were collected as test samples. These samples were carefully selected visually using very high-resolution images from Google Earth near the acquisition date of the satellite images because very high resolution land use/land cover maps from the local government were unavailable. The very high-resolution image from Google Earth, with a spatial resolution of 0.5 m, was acquired on February 20, 2008, which was approximately nine months before the imagery dates of the satellite images. We assumed that there were no significant land use/land cover changes during this period. Because there was a significant difference in the spatial resolution between the Google Earth image and the satellite images in this study, we did not apply a pixelto-pixel co-registration between the two data sources. Instead, we applied a visual interpretation to these images to select samples by incorporating the experiences of the authors, as we have been living within this urban area for years. These samples were randomly selected from six different land covers: dark impervious surface (DIS), bright impervious surface (BIS), vegetation (VEG), water (WAT), bare soil (BS) and shaded areas (SHA). To avoid possible bias among different land covers, we attempted to evenly select the samples over the entire study area and various land covers according to the layer and cluster sampling schema introduced by Jensen (2007). A detailed distribution of all of these samples is demonstrated in Table 2. Finally, the confusion matrix was used, and the overall accuracy (OA) and the Kappa coefficients were calculated to evaluate the estimation of impervious surfaces.

Table 2 Distribution of the training and test samples among various land covers. Land cover

Training samples (pixels)

Test samples (pixels)

VEG DIS BIS WAT SOI SHA Total

419 429 258 436 231 213 1986

397 569 459 496 359 355 2635

4. Results 4.1. Land cover classification using different optical and SAR data With the different combinations of optical and SAR image features in Table 1, six land covers could be classified in this study area. The confusion matrix-based accuracy was calculated to evaluate and compare the classification result using the test samples described in the methods section. Table 3 demonstrates the confusion matrix of the validation results, where some interesting findings can be observed. First, compared with using only SPOT data, using only ALOS or TSX data resulted in a significant decrease in the accuracy (Table 3(a)-(c)). Confusion between impervious surface covers and non-impervious surface covers increased with a decrease of OA and Kappa to 35.71% and 0.2205 for ALOS data only, to 35.10% and 0.2133 for ALOS data with polarimetric features, and to 36.20% and 0.2131 for TerraSAR-X data only. This significant decrease was primarily caused by the speckles in the SAR data. The result also shows that the higher spatial resolution of SAR data (TSX) may be a less negative result, while the confusion between vegetation and bright impervious surfaces and between shaded areas and bright impervious surfaces was greatly increased compared with lower resolution (ALOS) data. Second, optical and polarimetric SAR data can reduce the confusion between impervious surfaces and non-impervious surfaces with an increase in the accuracy. By comparing Table 3(a) and Table 3(e)-(g), it can be observed that the number of pixels of non-impervious surfaces (especially VEG and SHA) being misclassified as BIS were noticeably reduced. The best result was observed from the combination of SPOT and ALOS data with polarimetric features. Although the spatial resolution of TSX data is higher than ALOS data, it is single polarized, thus has a higher degree of confusion between the VEG and BIS. Third and consequently, after combining the SPOT and both the ALOS and TSX data in Table 3(h)-(i), the accuracy remained lower than that from combining the SPOT and ALOS data. 4.2. Comparison of different polarimetric features To further investigate the contributions of each polarimetric feature to impervious surface estimations, various polarimetric features were individually combined with SPOT data. The accuracy assessment results of this experiment are shown in Table 4. When

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Table 3 Confusion matrix and accuracy assessment of ISE using various combinations of data sets. (a) SPOT Only DIS DIS BIS VEG WAT BS SHA

DIS BIS VEG WAT BS SHA

DIS BIS VEG WAT BS SHA

DIS BIS VEG WAT BS SHA

DIS BIS VEG WAT BS SHA

BIS

(b) ALOS Only VEG

396 0 0 1 491 14 0 1 441 0 6 0 0 0 4 0 71 0 OA: 87.78%; Kappa: 0.8528 (c) ALOS(Pol) 125 125 89 150 248 104 22 68 63 41 25 47 58 23 5 1 80 151 OA: 35.10%; Kappa: 0.2133 (e) SPOT + ALOS 396 0 0 1 495 16 0 1 439 0 3 0 0 0 4 0 70 0 OA: 90.97%; Kappa: 0.8910 (g) SPOT + TSX 396 1 0 1 490 23 0 4 428 0 4 0 0 0 8 0 70 0 OA: 89.68%; Kappa: 0.8754 (i) SPOT + ALOS(Pol) + TSX 397 1 0 0 496 20 0 5 433 0 3 0 0 0 6 0 64 0 OA: 90.66%; Kappa: 0.8873

WAT

BS

SHA

DIS

0 0 0 412 0 84

42 0 69 0 248 0

9 21 0 0 0 325

83 10 1 354 46 2

126 52 28 89 47 17

63 133 53 13 5 88

0 0 0 489 0 7

40 0 66 0 253 0

11 19 0 0 0 325

0 0 0 481 0 15

44 0 71 0 244 0

13 18 0 0 0 324

123 119 83 150 260 107 18 64 72 44 25 47 60 20 7 2 81 143 OA: 35.71%; Kappa: 0.2205 (d) TSX Only 196 131 81 94 310 294 0 8 21 107 104 58 0 0 0 0 16 5 OA: 36.20%; Kappa: 0.2131 (f) SPOT + ALOS(Pol) 397 0 0 0 498 12 0 2 444 0 1 0 0 0 3 0 68 0 OA: 91.27%; Kappa: 0.8946 (h) SPOT + ALOS + TSX 397 2 0 0 488 19 0 5 433 0 4 0 0 0 7 0 70 0 OA: 90.51%; Kappa: 0.8855

0 0 0 493 0 3

41 0 70 0 248 0

17 16 0 0 0 322

Table 4 Accuracy assessment of land cover classification with various polarimetric features. Feature Combinations

Overall Accuracy

Kappa Coefficient

SPOT-5 Only ALOS Only SPOT and ALOS HH, HV SPOT and ALOS HH/HV SPOT and ALOS Alpha SPOT and ALOS Entropy (H) SPOT and ALOS Alpha-H SPOT and ALOS Coherence SPOT and ALOS All Features

87.78% 35.71% 90.97% 88.08% 88.54% 88.05% 88.73% 87.25% 91.27%

0.8528 0.2205 0.891 0.8564 0.8619 0.8559 0.8641 0.8464 0.8946

using only SPOT or ALOS data, the same results were fund as those discussed in Section 4.1. Nevertheless, we could observe the variations using different polarimetric features of dual-polarimetric ALOS data in Table 4. When using a single polarimetric feature, the HH/HV ratio and Entropy and Coherence did not show a significant contribution to the results compared to those found with SPOT-5 data only. However, when combining the Entropy and Alpha features, the accuracy was improved to an overall accuracy of 88.73% and a Kappa coefficient of 0.8641. Noticeably, if all of the polarimetric features were used together, the accuracy was improved to the highest observed, with an overall accuracy of 91.27% and a Kappa value of 0.8946. This accuracy was improved by approximately 3.5% compared to the accuracy using SPOT-5 data only.

BIS

VEG

WAT

BS

SHA

84 10 1 348 51 2

129 51 26 91 46 16

61 128 52 15 7 92

25 51 0 413 7 0

147 105 0 97 7 3

68 225 6 49 0 7

0 0 0 494 0 2

43 0 69 0 247 0

14 16 0 0 0 325

0 0 0 492 0 4

42 0 67 0 250 0

14 16 0 0 0 325

In addition, to further investigate the effect of various polarimetric features, the mapping result of impervious surfaces is shown in Fig. 2. This result shows the impervious surface estimation using dual polarimetric SAR and optical images, which are generally consistent. First, impervious surfaces extracted with only the PALSAR image (Fig. 2 (b)) show a high level of noise, especially over vegetated areas, due to the speckle phenomenon in SAR images (Fig. 1(b)). Thus, the impervious surface estimation result was not as good as that from the SPOT-5 image only (Fig. 2(a)). Second, the impervious surface estimated from various ALOS polarimetric features generally improved the results by reducing the confusion between shaded areas and water and dark impervious surfaces. However, because the shaded areas were mostly tall building shadows and were small spots in this study area, they are not significantly observed in Fig. 2. To further investigate the contribution of polarimetric SAR data and polarimetric features towards improving the accuracy of impervious surfaces mapping by addressing the confusion between impervious surfaces and non-impervious surfaces, the user’s accuracy and producer’s accuracy were calculated for three typical results corresponding to the use of SPOT-5 data only, combined SPOT-5 and backscattering coefficients of HH and HV from ALOS, and combined SPOT-5 and all of the polarimetric features from the ALOS data. Fig. 3 illustrates the experimental results, and we can observe a noticeable increase of the user’s accuracy for shade and the producer’s accuracy for water surfaces. The user’s accu-

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Fig. 2. Impervious surface estimation using SPOT-5 and various ALOS polarimetric features (White: impervious surface; black: non-impervious surface).

racy for dark impervious surfaces and the producer’s accuracy for both dark and bright impervious surface were slightly increased. These results indicate the effectiveness of polarimetric SAR data in addressing the confusion between dark impervious surfaces and water and shaded surfaces. On the other hand, both the user’s and producer’s accuracy for bare soil were only increased slightly by using polarimetric SAR data. Bright impervious surfaces and bare soil were not as easily confused with each other in a subtropical humid area, such as PRD, compared with those in other study areas

in temperate regions. Consequently, SAR data, which are sensitive to soil moisture, could not provide much information that improved the accuracy of bare soil identification. In general, the experiment in this study presents two important results that are related to the potential application of dualpolarimetric SAR data to impervious surface estimations and its incorporation with optical data. First, dual-polarimetric SAR data can provide better information than single polarization SAR data, although single polarization SAR data may have a greater spa-

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Fig. 3. User’s accuracy and Producer’s accuracy for three typical cases for five various land covers.

tial resolution. This finding indicates that multi-polarization may be more useful than spatial resolution when used for urban land cover classification. Second, however, multi-polarimetric SAR data should be handled with suitable polarimetric features extraction or decomposition because not every polarimetric feature can provide positive input towards the final classification result. For example, the joint use of Alpha and Entropy features provided better accuracy than the separate use of these two features (Table 4). This result indicates that different polarimetric features are not correlated to each other linearly and that the combination of all of these features would provide more beneficial information than using only a subset of them. Nevertheless, there are still some limitations of this study. First, the polarimetric SAR data used in this study were only dualpolarized, while the widely used fully polarized SAR data were not included. The major reason for this lack of inclusion is because of the nature of this study, which aimed at evaluating the dualpolarized SAR data, which were easier to access because of their simple data production. However, it would be useful to include fully polarized SAR data in this study to compare and investigate the usefulness of dual-polarized SAR data compared with fully polarized data in further applications. The constraint of this study is that the fully polarized SAR data were not available for the same study area. Second, there was only one study case in this experiment, which might reduce the significance of the conclusions. This shortcoming also arose from the availability of polarimetric SAR data. The research was designed to evaluate the single polarimetric and dual-polarimetric SAR data with different spatial resolutions. Consequently, the requirement of SAR data for this study was higher, and we could obtain a full data set in one small area of the PRD region. One possible solution is to divide the study area into two sub-areas. However, due to the similar patterns of urban land cover and other urban surface configurations, the results derived from this type of sub-area division might be dependent on each other to some extent. This interdependence might also reduce the significance of the possible outcomes. To overcome this shortcoming, our research group has started to conduct another collaborative study to obtain additional polarimetric SAR data in the areas of southeastern and northern parts of China. Hopefully, more experiments with sufficient data will be conducted with more statistically significant and more comprehensive results to deepen the applications of polarimetric SAR data in the observation of urban dynamics in the future.

5. Discussion The overall objective of this study is to explore the theoretical contribution of polarimetric SAR data to urban impervious surface mapping. To attain this goal, we applied a statistical experiment, such as a trial-and-error test, at the beginning stage to acquire more comprehensive polarimetric SAR data, such as fully polarized and compact polarized data. Our project team collected most of the required polarimetric SAR data in the urban areas of the PRD, with different polarimetric modes, covering different cities in this area. Therefore, we will attempt to address this problem in a future study. In the present study, we employed only dual-polarimetric SAR data to conduct a preliminary evaluation of the contribution of some polarimetric features, such as the coherence, the ratio of HH/HV and the Alpha-Entropy (H) decomposition features, which were reported to reflect the backscattering types of single/double bounce and volume backscattering. These backscattering mechanisms in the urban environment are particularly complex due to the complicated geometric structures of urban impervious features, such as tall buildings, highways and parking lots. In this first-stage study, our results demonstrated the effectiveness of improving the identification of water surfaces and shaded areas by incorporating polarimetric features to reduce the confusion with dark or bright impervious surfaces (as shown in Table 3 and Fig. 3). However, the direct identification of dark and bright impervious surfaces is much more complex due to the irregular directions of urban buildings. Although residential and commercial buildings are generally constructed in northsouth or northwest-southeast directions, many irregular buildings, especially commercial buildings, are constructed in other directions. This irregularity of buildings can produce sophisticated and mixed backscattering mechanisms of polarized microwaves, consequently causing more uncertainty towards the recognition of overall impervious materials. Theoretically, if we can successfully determine the mechanisms and processes between the interaction of polarized microwaves and various types of impervious surfaces, we can develop more sophisticated methods to exploit the polarimetric features while avoiding the disadvantages of these features. Achieving this goal requires comprehensive and comparative analysis on more polarimetric SAR data. In this study, an improvement by incorporating dual polarimetric data was achieved, although this improvement was not very significant (∼1% compared to using single polarized ALOS and TSX data). However, this improvement can be important from a technical viewpoint in impervious surfaces mapping as an input to various related urban remote sensing stud-

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ies. The key point is that we should conduct more experiments with various modes of polarimetric SAR data and over more comprehensive study areas, which is also the objective of our ongoing research project. 6. Conclusions Urban impervious surfaces have played an important role in various urban studies, such as urban environment, urban ecosystem and urban planning. Therefore, the accurate estimation of impervious surfaces is significant for these studies from local and regional to global scales. Due to the urban land cover diversity and the spectral confusion of these land covers, fusing optical and polarimetric SAR data has become a promising approach to improve urban impervious surfaces mapping. However, most studies have focused on using only single polarization SAR data. Multi-polarization SAR data, such as dual-polarized and full-polarized SAR data, have been proved to be useful in many other urban remote sensing studies, but they were seldom used to estimate impervious surfaces. This study aimed to present a preliminary experiment and a discussion on the combined use of multispectral optical data and dual polarization SAR data for improving the accuracy of impervious surfaces estimation. Our experiments using SPOT-5, TerraSAR-X and ALOS PALSAR images show a consistent result compared with our previous result using single polarization SAR data. Several polarimetric features, including HH/HV and Alpha-H polarimetric decomposition features and Coherence between different polarimetric data, were extracted and separated from the single polarization SAR images. Comparison results show that 1) multi-polarimetric SAR data provided better results than single polarization data, even though single polarization imagery may have higher spatial resolution; 2) not every polarimetric feature could provide a positive contribution to impervious surfaces mapping. Compared with using only optical data or only SAR data, the separated HH and HV polarization data provided a positive contribution to the result by improving the accuracy. The incorporation of both entropy and Alpha features also improved the accuracy. However, the HH/HV ratio and the separate use of entropy did not provide positive results. A combination of all of the features was capable of obtaining the highest accuracy compared with using a subset of the features. Acknowledgements This study is jointly supported by the Research Grants Council, General Research Fund (CUHK 14601515), National Natural Science Foundation of China (41401370), National Basic Research Program of China (2015CB954100) and CUHK Direct Grants (4052093). The authors would like to thank two anonymous reviewers and the editor for providing critical comments and suggestion that have significantly improved the original manuscript. References Ainsworth, T. L., Schuler, D. L., & Lee, J. S. (2008). Polarimetric SAR characterization of man-made structures in urban areas using normalized circular-pol correlation coefficients. Remote Sensing of Environment, 112(6), 2876–2885. Bhattacharya, A., & Touzi, R. (2011). Polarimetric SAR urban classification using the Touzi target scattering decomposition. Canadian Journal of Remote Sensing, 37(4), 323–332. Calabresi, G. (1996). The use of ERS data for flood monitoring: an overall assessment. pp. 237–241. London, UK: Second ERS Application Workshop. Cao, G. Z., & Jin, Y. Q. (2007). A hybrid algorithm of the BP-aNN/GA for classification of urban terrain surfaces with fused data of Landsat ETM+ and ERS-2 SAR. International Journal of Remote Sensing, 28(1–2), 293–305. Chen, J., & Quegan, S. (2011). Calibration of spaceborne CTLR compact polarimetric low-frequency SAR using mixed radar calibrators. IEEE Transactions on Geoscience and Remote Sensing, 49(7), 2712–2723.

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