Robust Dual Spatial Weighted Sparse Unmixing for Remotely Sensed Hyperspectral Imagery

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 16; p. 4056
• Main Authors: Deng, Chengzhi, Chen, Yonggang, Zhang, Shaoquan, Li, Fan, Lai, Pengfei, Su, Dingli, Hu, Min, Wang, Shengqian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Accuracy; Algorithms; Collaboration; data collection; double spatial weights; Geospatial data; hyperspectral imagery; Hyperspectral imaging; Image processing; Information processing; Libraries; Methods; multipliers; Regularization; Remote sensing; risk; Robustness; Smoothness; sparse hyperspectral unmixing; Sparsity; Spatial data; spatial information; superpixel segmentation; Weighting; China

Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries. In recent years, there has been a growing trend in incorporating spatial information from hyperspectral images into sparse unmixing...