Hyperspectral Image Data Reduction for Endmember Extraction
This work addresses a bottleneck for researchers and practitioners in remote sensing by making large-scale hyperspectral image analysis more efficient, though it is incremental as it builds on existing self-dictionary methods.
The paper tackles the high computational cost of self-dictionary methods for endmember extraction in hyperspectral images by developing a data reduction technique that removes non-endmember pixels, resulting in substantially reduced computational time without sacrificing accuracy.
Endmember extraction from hyperspectral images aims to identify the spectral signatures of materials present in a scene. Recent studies have shown that self-dictionary methods can achieve high extraction accuracy; however, their high computational cost limits their applicability to large-scale hyperspectral images. Although several approaches have been proposed to mitigate this issue, it remains a major challenge. Motivated by this situation, this paper pursues a data reduction approach. Assuming that the hyperspectral image follows the linear mixing model with the pure-pixel assumption, we develop a data reduction technique that removes pixels that do not contain endmembers. We analyze the theoretical properties of this reduction step and show that it preserves pixels that lie close to the endmembers. Building on this result, we propose a data-reduced self-dictionary method that integrates the data reduction with a self-dictionary method based on a linear programming formulation. Numerical experiments demonstrate that the proposed method can substantially reduce the computational time of the original self-dictionary method without sacrificing endmember extraction accuracy.