Unmixing method for hyperspectral data based on sub-space method with learning process |
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Authors: | Kohei Arai Huahui Chen |
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Affiliation: | Department of Information Science, Saga University, 1 Honjo, Saga 840-8502, Japan |
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Abstract: | An unmixing method for hyperspectral Earth observation satellite imagery data is proposed. It is based on a sub-space method with learning process. The proposed method utilizes a sub-space for feature space during unmixing. It is used to be done in a feature space which consists of spectral bands of observation vectors. As the results from the experiments with airborne based hyperspectral imagery data, AVIRIS, it is found that the proposed unmixing is superior to the other existing method in terms of decomposition accuracy and the process time required for the decompositions. |
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Keywords: | Unmixing Category decomposition Hyperspectral data Sub-space method Learning process AVIRIS |
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