Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area

Abstract

Abstract:A novel approach is proposed for classifying the polarimetric SAR (PolSAR) data by integrating polarimetric decomposition, sub-aperture decomposition and decision tree algorithm. It is composed of three key steps: sub-aperture decomposition, feature extraction and combination, and decision tree classification. Feature extraction and combination is the main contribution to the innovation of the proposed method. Firstly, the full-resolution PolSAR image and its two sub-aperture images are decomposed to obtain the scattering entropy, average scattering angle and anisotropy, respectively. Then, the difference information between the two sub-aperture images are extracted, and combined with the target decomposition features from full-resolution images to form the classification feature set. Finally, C5.0 decision tree algorithm is used to classify the PolSAR image. A comparison between the proposed method and commonly-used Wishart supervised classification was made to verify the improvement of the proposed method on the classification. The overall accuracy using the proposed method was 88.39%, much higher than that using the Wishart supervised classification, which exhibited an overall accuracy of 69.82%. The Kappa Coefficient was 0.83, whereas that using the Wishart supervised classification was 0.56. The results indicate that the proposed method performed better than Wishart supervised classification for landscape classification in urban area using PolSAR data. Further investigation was carried out on the contribution of difference information to PolSAR classification. It was found that the sub-aperture decomposition improved the classification accuracy of forest, buildings and grassland effectively in high-density urban area. Compared with support vector machine (SVM) and QUEST OPEN ACCESSRemote Sens. 2015, 7 1381 classifier, C5.0 decision tree classifier performs more efficient in time consumption, feature selection and construction of decision rule.

Support subspaces method for synthetic aperture radar automatic target recognition

“…It can be explained by the fact that in cloudy weather, the use of SAR gives better data of observed ground objects than visible, near-infrared and radar satellite data. [1][2][3] For example, Defense Advanced Research Projects Agency (DARPA) obtained SAR images of ground targets in different poses, which contain the moving and stationary target acquisition and recognition (MSTAR) public release data set, 4 which many researchers use. [5][6][7][8][9][10][11][12][13][14][15] In the majority of cases, they apply a double-stage approach for object recognition.…”

“…It is applied in optical, multispectral, hyperspectral image and LiDAR data classification. 17,18 In the works described above, [1][2][3] SVM was applied for land-use classification of a set of satellite images. In the study by Liu and Feng,19 faces were recognized using SVM with different kernel functions.…”

Built-up Area Extraction from PolSAR Imagery with Model-Based Decomposition and Polarimetric Coherence

“…else Natural areas (19) where Th d is the threshold of double-bounce scattering power. Condition 1 is used to extract the urban areas on the basis of decomposed scattering powers.…”

PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain

“…Deng et al used both polarimetric decomposition and time-frequency decomposition to mine the hidden information of objects in PolSAR images and applied a C5.0 decision tree (DT) algorithm for optimal feature selection and final classification [19]. They also proposed an approach to classify the PolSAR data by integrating polarimetric decomposition, sub-aperture decomposition, and DT algorithm [20]. Cheng et al designed and implemented a segmentation-based PolSAR image classification method incorporating texture features, color features and SVM classifier [21].…”