The following publications are possibly variants of this publication:
- Hyperspectral unmixing by reweighted low rank and total variationRui Wang, Wenzhi Liao, Heng-Chao Li, Hongyan Zhang, Aleksandra Pizurica. whispers 2016: 1-4 [doi]
- Multidimensional Low-Rank Representation for Sparse Hyperspectral UnmixingLing Wu, Jie Huang 0005, Ming-Shuang Guo. lgrs, 20:1-5, 2023. [doi]
- Robust GBM hyperspectral image unmixing with superpixel segmentation based low rank and sparse representationXiaoguang Mei, Yong Ma, Chang Li, Fan Fan, Jun Huang, Jiayi Ma 0001. ijon, 275:2783-2797, 2018. [doi]
- Reweighted sparse unmixing for hyperspectral images with noise level estimationSi Wang, Ting-Zhu Huang, Xi-Le Zhao, Jie Huang 0005. jcam, 421:114843, 2023. [doi]
- Reweighted Sparse Regression for Hyperspectral UnmixingCheng Yong Zheng, Hong Li, Qiong Wang, C. L. Philip Chen. tgrs, 54(1):479-488, 2016. [doi]
- Joint-Sparse-Blocks and Low-Rank Representation for Hyperspectral UnmixingJie Huang, Ting-Zhu Huang, Liang-Jian Deng, Xi-Le Zhao. tgrs, 57(4):2419-2438, 2019. [doi]
- Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank RepresentationYong Ma 0001, Qiwen Jin, Xiaoguang Mei, Xiaobing Dai, Fan Fan 0001, Hao Li, Jun Huang. remotesensing, 11(8):911, 2019. [doi]
- Gaussian Mixture Model for Hyperspectral Unmixing with Low-Rank RepresentationQiwen Jin, Yong Ma 0001, Xiaoguang Mei, Xiaobing Dai, Hao Li, Fan Fan 0001, Jun Huang. igarss 2019: 294-297 [doi]