Efficient Hyperspectral Sparse Regression Unmixing With Multilayers
Xiangfei Shen, Lihui Chen, Haijun Liu, Xi Su, Wenjia Wei, Xia Zhu, Xichuan Zhou
Abstract
The sparse regression method is known for its ability to unmix hyperspectral data, but it can be computationally expensive and accurately insufficient due to the large scale and high coherence of the spectral library. To address this issue, a new approach called layered sparse regression unmixing (termed LSU) has been proposed in this paper. This method involves breaking down the sparse unmixing process into multilayers, each of which interactively learns a row-sparsity-promoting abundance matrix and fine-tunes active library atoms based on measured activeness. By doing so, LSU outputs both a learned abundance matrix and an optimal library that can best model each mixed pixel in the scene. The proposed LSU can be efficiently solved by the alternating direction method of the multipliers framework. Experimental results obtained from simulated and real hyperspectral images demonstrate the effectiveness of LSU. The demo of the proposed LSU will be publicly available at https://github.com/XiangfeiShen/Layered_Sparse_Regression_Unmixing.