23 February 2005 Independent component analysis for hyperspectral imagery plant classification
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In the using of remote sensing technology for agriculture investigation, it is often required to extract plant and vegetation distribution from land covers, especially when plants are sparsely dispersed. Otherwise it would consume considerable expense to perform ground survey. Due to its narrow spectral bandwidth and high spectral resolution, hyperspectral remote sensing technique is regarded as a suitable technique for such task. Conventional classification techniques often suffer from unrealistic mathematical distribution assumption, and are also subject to the lack of prerequisite information. Trying to address the problem of unsurprised plant classification, this paper proposes an Independent Component Analysis (ICA) based feature extraction algorithm. ICA is a technique that stems out from the Blind Source Separation. In hyperspectral data processing, ICA projects the vector to the space where the items of the vector are mutually statistically independent, and therefore is capable of extracting various kinds of information from imagery. Besides, so as to strengthen the contrast of result independent component, a mathematical morphology based post-processing procedure is appended after ICA decomposition. Through real hyperspectral data experiments, our algorithm has demonstrated better performance in classification. Computation efficiency and noise robustness have also been improved by an extra noise filtering effort.
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Peng Du, Peng Du, Huijie Zhao, Huijie Zhao, Bing Zhang, Bing Zhang, Lanfen Zheng, Lanfen Zheng, } "Independent component analysis for hyperspectral imagery plant classification", Proc. SPIE 5673, Applications of Neural Networks and Machine Learning in Image Processing IX, (23 February 2005); doi: 10.1117/12.584356; https://doi.org/10.1117/12.584356

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