This paper proposes a method of polarimetric synthetic aperture radar (PolSAR) image classification using improved superpixel segmentation and manifold learning. Firstly, a 27-dimension polarimetric feature space is extracted by simple arithmetic operations of polarimetric parameters and polarimetric target decomposition. Secondly, Laplacian Eigenmap (LE) algorithm is used to reduce the dimension of the 27-dimension polarimetric features. This algorithm can reduce redundant information in feature space and extract the main information. Then, the paper uses SVM which has the best classification performance to classify the low-dimension PolSAR data for the first time. And then, the superpixel segmentation is obtained by improving SLIC algorithm. At last, the majority voting principle is used to classify the superpixel blocks, which is the second classification and final classification of PolSAR data.
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PolSAR image classification based on Laplacian Eigenmaps and superpixels
Semantic Scholar · Computer Science · 2017
Abstract
This paper proposes a method of polarimetric synthetic aperture radar (PolSAR) image classification using improved superpixel segmentation and manifold learning. Firstly, a 27-dimension polarimetric feature space is extracted by simple arithmetic operations of polarimetric parameters and polarimetric target decomposition. Secondly, Laplacian Eigenmap (LE) algorithm is used to reduce the dimension of the 27-dimension polarimetric features. This algorithm can reduce redundant information in feature space and extract the main information. Then, the paper uses SVM which has the best classification performance to classify the low-dimension PolSAR data for the first time. And then, the superpixel segmentation is obtained by improving SLIC algorithm. At last, the majority voting principle is used to classify the superpixel blocks, which is the second classification and final classification of PolSAR data.