Spatial-Aware Dictionary Learning for Hyperspectral Image Classification

TL;DR

This paper introduces a structured dictionary learning approach that leverages spectral and spatial context for improved hyperspectral image classification, demonstrating effectiveness on real and simulated data.

Contribution

It proposes a novel spatial-aware dictionary learning model that incorporates contextual groupings and joint sparsity for hyperspectral classification.

Findings

Effective classification on real hyperspectral images.

Capable of representing multispectral data for classification.

Outperforms traditional methods in experiments.

Abstract

This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of a spectral sample, with the goal of hyperspectral image classification. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model each pixel with a linear combination of a few dictionary elements learned from the data. Since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the dictionary. To this end, dictionary learning is carried out with a joint sparse regularizer to induce a common sparsity pattern in the sparse coefficients of each contextual group. The sparse coefficients are then used for classification using a linear SVM. Experimental results on a number of real hyperspectral images confirm the effectiveness of the proposed representation for hyperspectral image classification. Moreover, experiments with simulated multispectral data show that the proposed model is capable of finding representations that may effectively be used for classification of multispectral-resolution samples.