Shapelet-based Sparse Representation for Landcover Classification of Hyperspectral Images

TL;DR

This paper introduces a novel shapelet-based sparse representation method for landcover classification in hyperspectral images, integrating spatial and spectral information through an unsupervised learned dictionary, leading to improved classification accuracy.

Contribution

The paper proposes a new dictionary construction method using shapelets and spectral data, enhancing sparse representation-based classification of hyperspectral images.

Findings

Outperforms traditional sparse classifiers with limited spatial info

Competitive with or better than state-of-the-art spatial classifiers

Demonstrates superior accuracy on multiple hyperspectral datasets

Abstract

This paper presents a sparse representation-based classification approach with a novel dictionary construction procedure. By using the constructed dictionary sophisticated prior knowledge about the spatial nature of the image can be integrated. The approach is based on the assumption that each image patch can be factorized into characteristic spatial patterns, also called shapelets, and patch-specific spectral information. A set of shapelets is learned in an unsupervised way and spectral information are embodied by training samples. A combination of shapelets and spectral information are represented in an undercomplete spatial-spectral dictionary for each individual patch, where the elements of the dictionary are linearly combined to a sparse representation of the patch. The patch-based classification is obtained by means of the representation error. Experiments are conducted on three well-known hyperspectral image datasets. They illustrate that our proposed approach shows superior results in comparison to sparse representation-based classifiers that use only limited spatial information and behaves competitively with or better than state-of-the-art classifiers utilizing spatial information and kernelized sparse representation-based classifiers.