Endmember Extraction from Hyperspectral Images Using Self-Dictionary Approach with Linear Programming

TL;DR

This paper introduces an improved Hottopixx method using linear programming for hyperspectral endmember extraction, reducing computational costs and enhancing accuracy for real-world applications.

Contribution

The paper presents an enhanced implementation of the self-dictionary LP-based Hottopixx method that is more computationally efficient and effective for practical hyperspectral endmember extraction.

Findings

Reduced computational time compared to traditional methods

Achieved high accuracy in endmember signature estimation

Enabled application to real hyperspectral images

Abstract

Hyperspectral imaging technology has a wide range of applications, including forest management, mineral resource exploration, and Earth surface monitoring. A key step in utilizing this technology is endmember extraction, which aims to identify the spectral signatures of materials in observed scenes. Theoretical studies suggest that self-dictionary methods using linear programming (LP), known as Hottopixx methods, are effective in extracting endmembers. However, their practical application is hindered by high computational costs, as they require solving LP problems whose size grows quadratically with the number of pixels in the image. As a result, their actual effectiveness remains unclear. To address this issue, we propose an enhanced implementation of Hottopixx designed to reduce computational time and improve endmember extraction performance. We demonstrate its effectiveness through experiments. The results suggest that our implementation enables the application of Hottopixx for endmember extraction from real hyperspectral images and allows us to achieve reasonably high accuracy in estimating endmember signatures.