Abstract
Synthetic aperture radar (SAR) image classification is an important task in remote sensing applications. However, it is challenging due to the speckle embedding in SAR imaging, which significantly degrades the classification performance. To address this issue, a new SAR image classification framework based on multi-feature fusion and adaptive kernel combination is proposed in this paper. Expressing pixel similarity by non-negative logarithmic likelihood difference, the generalized neighborhoods are newly defined. The adaptive kernel combination is designed on them to dynamically explore multi-feature information that is robust to speckle noise. Then, local consistency optimization is further applied to enhance label spatial smoothness during classification. By simultaneously utilizing adaptive kernel combination and local consistency optimization for the first time, the texture feature information, context information within features, generalized spatial information between features, and complementary information among features is fully integrated to ensure accurate and smooth classification. Compared with several state-of-the-art methods on synthetic and real SAR images, the proposed method demonstrates better performance in visual effects and classification quality, as the image edges and details are better preserved according to the experimental results.
Highlights
Synthetic aperture radar (SAR) can acquire images of different land covers through clouds and rain in all weather conditions and times, and it has a certain surface penetrating capability
The experiments were conducted on a laptop computer with an Intel Core i5 2.6-GHz CPU and 16-GB memory; the algorithms were implemented in MATLAB 2016b
CKS-FCM; (e1–e3) composite kernel feature fusion (CKFF); (f1–f3) support vector machine (SVM)-CK; (g1–g3) multi-feature weighted sparse graph (MWSG); (h1–h3) multi-feature fusion and adaptive kernel combination (MAKC), where the numbers represent the index of the images
Summary
Synthetic aperture radar (SAR) can acquire images of different land covers through clouds and rain in all weather conditions and times, and it has a certain surface penetrating capability. SAR image classification it is not easy to obtain satisfactory results, due to the speckle embedding in SAR imaging. This remains a challenging task to be resolved. The research on SAR image classification technology has mainly focused on two crucial aspects: more effective representation of feature information, and enhancing the local consistency on pixel labels. We briefly review texture information representations of SAR images, which are gray-level co-occurrence matrix (GLCM), wavelet (WL), and attribute profile (AP). A gray-level co-occurrence matrix (GLCM) can express the imaging mechanism and the statistical characteristics of SAR images [20,21]. The 3D GLCM feature tensor of I1 × I2 × 6 size is obtained for the SAR image, which is
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