8 February 2001 New progress in study on vegetation models for hyperspectral remote sensing
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Abstract
Some new vegetation models for hyperspectral remote sensing are provided in this paper. They are Derivative Spectral Model (DSM), Multi-temporal Index Image Cube Model (MIIC), Hybrid Decision Tree Model (HDT) and Correlation Simulating Analysis Model (CSAM). All models are developed and used to process the images acquired by Airborne Pushbroom Hyperspectral Imager (PHI) in Changzhou area, China, 1999. Some successful applications are provided and evaluated. The results show that DSM has the ability of eliminating the background interference of vegetation analysis. MIIC is a viable method for monitoring dynamic change of land cover and vegetation growth stages. HDT is effective in precise classification of rice land while CSAM provide a possibility and theoretical basis for crop identification, breed classification, and land information extraction especially for rice.
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Qingxi Tong, Yongchao Zhao, Xia Zhang, Bing Zhang, and Lanfen Zheng "New progress in study on vegetation models for hyperspectral remote sensing", Proc. SPIE 4151, Hyperspectral Remote Sensing of the Land and Atmosphere, (8 February 2001); doi: 10.1117/12.417002; https://doi.org/10.1117/12.417002
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