Adaptive kernel sparse representation based on multiple feature learning for hyperspectral image classification

Abstract

For hyperspectral image classification, this paper proposes a novel adaptive kernel sparse representation method based on multiple feature learning (AKSR-MFL). Firstly, multiple types of feature, including different kinds of spectral and spatial information, are extracted from the original HSI to describe the characteristics of pixels from different perspectives, which is beneficial to enhance the classification accuracy significantly. To further explore contextual information and conform the spatial structure as far as possible, we employ shape-adaptive algorithm to construct a shape-adaptive region for each test pixel at the same time. Then, we design adaptive kernel sparse representation (AKSR) method by applying kernel joint sparse pattern to address the linearly inseparable problem of classification in multiple feature space and make the pixels with the same distribution more easily grouped and linearly separable. Moreover, composite kernel constructed by multiple kernel learning (MKL) is embedded into AKSR to effectively construct base kernels for different feature descriptors and determine the weights of base kernels optimally, which can take the similarity and diversity of different types of feature descriptor into full consideration. Experimental results on three widely used real HSI data demonstrate that the proposed AKSR-MFL classifier outperforms several state-of-the-art classification methods.