Hyperspectral Image Classification and Clutter Detection via Multiple Structural Embeddings and Dimension Reductions
This addresses challenges in hyperspectral image analysis for remote sensing applications, but it appears incremental as it builds on existing embedding and dimension reduction techniques.
The authors tackled hyperspectral image classification and clutter detection by generating an adaptive, structurally enriched representation environment using locally linear embedding with external feature space and parameter layers, achieving remarkably high-accuracy classification results on two datasets with a small number of learning samples.
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.