Land Cover Classification from Hyperspectral Images via Weighted Spatial–Spectral Joint Kernel Collaborative Representation Classifier

Abstract

The continuous changes in Land Use and Land Cover (LULC) produce a significant impact on environmental factors. Highly accurate monitoring and updating of land cover information is essential for environmental protection, sustainable development, and land resource planning and management. Recently, Collaborative Representation (CR)-based methods have been widely used in land cover classification from Hyperspectral Images (HSIs). However, most CR methods consider the spatial information of HSI by taking the average or weighted average of spatial neighboring pixels of each pixel to improve the land cover classification performance, but do not take the spatial structure information for pixels into account. To address this problem, a novel Weighted Spatial–Spectral Joint CR Classification (WSSJCRC) method is proposed in this paper. WSSJCRC only performs spatial filtering on HSI through a weighted spatial filtering operator to alleviate the spectral shift caused by adjacency effect, but also utilizes the labeled training pixels to simultaneously represent each test pixel and its spatial neighborhood pixels to consider the spatial structure information of each test pixel to assist the classification of the test pixel. On this basis, the kernel version of WSSJCRC (i.e., WSSJKCRC) is also proposed, which projects the hyperspectral data into the kernel-induced high-dimensional feature space to enhance the separability of nonlinear samples. The experimental results on three real hyperspectral scenes show that the proposed WSSJKCRC method achieves the best land cover classification performance among all the compared methods. Specifically, the Overall Accuracy (OA), Average Accuracy (AA), and Kappa statistic (Kappa) of WSSJKCRC reach 96.21%, 96.20%, and 0.9555 for the Indian Pines scene, 97.02%, 96.64%, and 0.9605 for the Pavia University scene, and 95.55%, 97.97%, and 0.9504 for the Salinas scene, respectively. Moreover, the proposed WSSJKCRC method obtains the promising accuracy with OA over 95% on the three hyperspectral scenes under the situation of small-scale labeled samples, thus effectively reducing the labeling cost for HSI.