Spatial–Spectral Adaptive Learning With Pixelwise Filtering for Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) classification is significant in remote sensing applications. However, most methods focus on the spectral and spatial correlation information in the neighborhood while ignoring the feature difference among global different pixels. In this article, we propose a spatial–spectral adaptive learning with pixelwise filtering (SSALPF) method to fully consider the discriminative information of pixels in different spatial locations, which mainly consists of a parallel spatial–spectral adaptive learning (SSAL) module and a pixelwise filtering (PF) module. Specifically, the former aims to obtain joint spatial–spectral discriminative features of each pixel point in a parallel manner and is used as a guide for adaptive selection of filter kernel. The latter uses the adaptive filter kernel to implement pixel-level filtering on HSI, in order to learn the discriminative features contained in different pixel points for classification. The adaptive filter kernel is generated by a linear combination of a predefined dictionary containing multiple filter bases. Experiments demonstrate that the proposed method is superior to other methods on popular hyperspectral datasets.