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• W4386065634 abstract "Traditional spectral unmixing (SU) of satellite hyperspectral images (HSIs) faces two main challenges: One is that limited by the low resolution of satellite HSIs, it is difficult to guarantee the accuracy of endmember extraction due to severe spectral mixing; the other is that the spectral variability is unavoidable due to external factors such as atmospheric, illumination, and topographic variations, as well as internal factors such as physical changes of the features themselves. Unmanned aerial vehicle (UAV) HSIs of high spatial resolution can provide a highly accurate reflectance curves from regions of interest (ROIs), and the intrinsic image decomposition (IID) technique can reduce the spectral variability caused by external factors. Based on this, a novel IID embedded UAV-satellite spectral unmixing model is proposed. On the one hand, the spectral variability is solved by an embedded IID framework in the inverse problem of SU. The proposed method replaces the input, <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>i.e</i> ., the original HSI, with the reflectance component, which is independent of the spectral variability caused by external factors. On the other hand, a UAV spectral library constructed from the UAV HSI is introduced to guarantee the accuracy of the endmember. Thus, by IID embedded in the framework of UAV-satellite collaborative spectral unmixing, the proposed method is able to address the aforementioned problems. Experimental validation is conducted using UAV HSI and three sets of satellite HSI from the Yellow River Delta region. The results indicate that the proposed method can effectively improve the robustness and superiority of the unmixing results." @default.
• W4386065634 created "2023-08-23" @default.
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• W4386065634 date "2023-01-01" @default.
• W4386065634 modified "2023-10-16" @default.
• W4386065634 title "Intrinsic Decomposition Embedded Spectral Unmixing for Satellite Hyperspectral Images with Endmembers from UAV Platform" @default.
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• W4386065634 doi "https://doi.org/10.1109/tgrs.2023.3307346" @default.
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