Random Subspace-Based k-Nearest Class Collaborative Representation for Hyperspectral Image Classification

Recently, collaborative representation classification (CRC) has attracted extensive interest for hyperspectral images (HSIs) classification. However, for collaborative representation with Tikhonov (CRT), a testing sample is collaboratively represented by training samples from all the classes, which may result in high computational cost. In this article, we select the first k class training samples that are nearest to the testing sample for representation, namely, k-nearest class CRT (KNCCRT) algorithm. In order to improve the performance of KNCCRT for HSI classification, the idea of random subspace-based KNCCRT ensemble framework is proposed. KNCCRT is adopted as base classifier and random subspace (RS) contributes to diversity by selecting feature randomly. Moreover, to further increase the classification accuracy, shape-adaptive (SA) neighborhood constraint is utilized in RS ensemble framework to incorporate spatial information. Experimental results on three real hyperspectral data sets demonstrate the effectiveness of the proposed methods for HSI classification. The combination of KNCCRT and RS framework provides a reliable accuracy for HSI classification.

Automated summary

This article introduces a new approach for hyperspectral image classification, which combines the KNCCRT and random subspace (RS) framework. Experimental results demonstrate the effectiveness of this method for HSI classification, improving classification accuracy.