Endmember Extraction from Hyperspectral Images Using Self-Dictionary Approach with Linear Programming
This work addresses a computational bottleneck for researchers and practitioners in fields like forest management and mineral exploration, but it is incremental as it builds on existing Hottopixx methods.
The paper tackled the high computational cost of Hottopixx methods for endmember extraction in hyperspectral images by proposing an enhanced implementation, which reduced computational time and achieved reasonably high accuracy in estimating endmember signatures.
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.