Abstract
The composite kernel feature fusion proposed in this paper attempts to solve the problem of classifying polarimetric synthetic aperture radar (PolSAR) images. Here, PolSAR images take into account both polarimetric and spatial information. Various polarimetric signatures are collected to form the polarimetric feature space, and the morphological profile (MP) is used for capturing spatial information and constructing the spatial feature space. The main idea is that the composite kernel method encodes diverse information within a new kernel matrix and tunes the contribution of different types of features. A support vector machine (SVM) is used as the classifier for PolSAR images. The proposed approach is tested on a Flevoland PolSAR data set and a San Francisco Bay data set, which are in fine quad-pol mode. For the Flevoland PolSAR data set, the overall accuracy and kappa coefficient of the proposed method, compared with the traditional method, increased from 95.7% to 96.1% and from 0.920 to 0.942, respectively. For the San Francisco Bay data set, the overall accuracy and kappa coefficient of the proposed method increased from 92.6% to 94.4% and from 0.879 to 0.909, respectively. Experimental results verify the benefits of using both polarimetric and spatial information via composite kernel feature fusion for the classification of PolSAR images.
Highlights
Polarimetric synthetic aperture radar (PolSAR) has become an important remote sensing tool.Besides the advantage of operating in all times and under all weather conditions, it provides richer ground information than single-polarization SAR [1]
Experimental results demonstrate that the proposed approach can more efficiently exploit both the polarimetric and the spatial information contained in PolSAR images compared with the traditional method of feature fusion
For original single-look complex (SLC) PolSAR images, at each pixel, the data is stored in the scattering matrix
Summary
Polarimetric synthetic aperture radar (PolSAR) has become an important remote sensing tool. Inspired by the complementarity between spatial and spectral features producing significant improvements in optical image classification [7], in this paper, the main characteristic of the proposed approach is that it takes advantage of both polarimetric and spatial information for classification. The main problem here is how to make comprehensive use of the two types of features In this paper, this problem is solved based on the theory of feature fusion via composite kernels [19]. Experimental results demonstrate that the proposed approach can more efficiently exploit both the polarimetric and the spatial information contained in PolSAR images compared with the traditional method of feature fusion.
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