Recently, Liu Erfeng, a postgraduate for master degree in system science of the Business School, published a paper entitled “A Multi-objective Method Leveraging Spatial–Spectral Relationship for Hyperspectral Unmixing” in IEEE Transactions on Geoscience and Remote Sensing (IEEE TGRS), one of the top journals in the field of earth science and remote sensing. Liu Erfeng, a 2020 postgraduate assumed the first author, Associate Professor Wu Zikai is the corresponding author, and Associate Professor Zhang Hongjuan, of Shanghai University is the co-corresponding author.
Aiming at the problem of hyperspectral image unmixing, this paper constructs a multi-objective optimization model considering spectral information and spatial information at the same time, and introduces the Euclidean distance, spectral angle and spectral information divergence of spectral signal into the algorithm solving process to effectively improve the unmixing accuracy. Through experimental analysis, the proposed method obtains a higher signal to reconstruction error ratio, and reconstructs a clearer abundance image.
Wu Zikai is a member of the system biology innovation team. The above research has been strongly supported by the system biology innovation team construction project.
IEEE TGRS is one of the top journals in the field of geoscience and remote sensing. It is the official journal of the IEEE Association for Geoscience and Remote Sensing Technology (GRSS). It has a high influence in the field of remote sensing technology and geoscience. The latest impact factor in 2022 is 8.125. At present, it is a sub category I journal (Geochemistry and Geophysics), a sub category II journal, and a top journal of the Chinese Academy of Sciences.
Article information: E. Liu, Z. Wu and H. Zhang, “A Multi-objective Method Leveraging Spatial–Spectral Relationship for Hyperspectral Unmixing,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022, Art no. 5539416, doi: 10.1109/TGRS.2022.3210198.
