HyperSpectral classification with adaptively weighted L1-norm regularization and spatial postprocessing

TL;DR

This paper introduces a new hyperspectral image classification method that combines adaptive L1-norm regularization, kernelization, and spatial postprocessing, demonstrating competitive performance with existing state-of-the-art algorithms.

Contribution

The paper develops a convex hyperspectral classification method using adaptive L1 regularization, kernelization, and spatial processing, improving upon existing sparse unmixing techniques.

Findings

Competitive with state-of-the-art algorithms like SVM-CK and KSOMP-CK.

Enhanced class separability through spatial pre and post-processing.

Effective use of adaptive L1-norm regularization in hyperspectral classification.

Abstract

Sparse regression methods have been proven effective in a wide range of signal processing problems such as image compression, speech coding, channel equalization, linear regression and classification. In this paper a new convex method of hyperspectral image classification is developed based on the sparse unmixing algorithm SUnSAL for which a pixel adaptive L1-norm regularization term is introduced. To further enhance class separability, the algorithm is kernelized using an RBF kernel and the final results are improved by a combination of spatial pre and post-processing operations. It is shown that the proposed method is competitive with state of the art algorithms such as SVM-CK, KSOMP-CK and KSSP-CK.