Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images

TL;DR

This paper presents ADVIS, an active learning method for hyperspectral image segmentation that uses diffusion distance to select representative pixels for labeling, significantly improving material discrimination accuracy.

Contribution

Introduction of ADVIS, a novel active learning approach leveraging diffusion distance for efficient hyperspectral image segmentation with minimal labeled data.

Findings

ADVIS outperforms unsupervised clustering in material discrimination.

Few carefully-selected labels lead to substantial accuracy improvements.

Diffusion distance effectively guides pixel selection for labeling.

Abstract

Hyperspectral images encode rich structure that can be exploited for material discrimination by machine learning algorithms. This article introduces the Active Diffusion and VCA-Assisted Image Segmentation (ADVIS) for active material discrimination. ADVIS selects high-purity, high-density pixels that are far in diffusion distance (a data-dependent metric) from other high-purity, high-density pixels in the hyperspectral image. The ground truth labels of these pixels are queried and propagated to the rest of the image. The ADVIS active learning algorithm is shown to strongly outperform its fully unsupervised clustering algorithm counterpart, suggesting that the incorporation of a very small number of carefully-selected ground truth labels can result in substantially superior material discrimination in hyperspectral images.