Hyperspectral Image Classification and Clutter Detection via Multiple Structural Embeddings and Dimension Reductions

TL;DR

This paper introduces a novel hyperspectral image classification and clutter detection method that uses multiple structural embeddings and dimension reduction techniques to improve accuracy and robustness in noisy, high-dimensional data.

Contribution

It develops an adaptive, structurally enriched representation combined with locally linear embedding, effectively addressing spectral correlation, nonlinear structure, and uncertainty in hyperspectral data.

Findings

Achieved high-accuracy classification on two HSI datasets.

Produced clear clutter detection maps.

Demonstrated robustness with limited training samples.

Abstract

We present a new and effective approach for Hyperspectral Image (HSI) classification and clutter detection, overcoming a few long-standing challenges presented by HSI data characteristics. Residing in a high-dimensional spectral attribute space, HSI data samples are known to be strongly correlated in their spectral signatures, exhibit nonlinear structure due to several physical laws, and contain uncertainty and noise from multiple sources. In the presented approach, we generate an adaptive, structurally enriched representation environment, and employ the locally linear embedding (LLE) in it. There are two structure layers external to LLE. One is feature space embedding: the HSI data attributes are embedded into a discriminatory feature space where spatio-spectral coherence and distinctive structures are distilled and exploited to mitigate various difficulties encountered in the native hyperspectral attribute space. The other structure layer encloses the ranges of algorithmic parameters for LLE and feature embedding, and supports a multiplexing and integrating scheme for contending with multi-source uncertainty. Experiments on two commonly used HSI datasets with a small number of learning samples have rendered remarkably high-accuracy classification results, as well as distinctive maps of detected clutter regions.