8 December 2011 Evaluation of hyperspectral classification methods based on FISS data
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Proceedings Volume 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis; 80020L (2011) https://doi.org/10.1117/12.902908
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China
Abstract
With the deterioration of ecological environment, rare plants on the earth are decreasing rapidly, so there is an urgent need for the study on sophisticated vegetation classification. Hyperspectral data have great potential in sophisticated classification. FISS(Field Imaging Spectrometer System) is a newly developed system, and pixels of FISS could be considered as pure pixels with high spatial and spectral resolution, which makes FISS a perfect option on the study of methodology. This study aims to evaluate different methods based on FISS data and find out the best one of sophisticated vegetation classification. The methods are as follows: Maximum Likelihood (ML), Spectral Angle Mapping (SAM), Artificial Neural Net (ANN), Support Vector Machine (SVM) and Composite Kernel Support Vector Machine (C-SVM). Firstly, segmented principal components transformation is adopted for spectral dimensionality reduction, and all bands are divided into 2 subsets according to the correlation matrix. Secondly, 16 principal components are kept. After that, 5 methods mentioned above are tested. The Overall Accuracy and Kappa coefficient of C-SVM, SVM and ANN are higher than 90%, and C-SVM obtains the highest accuracy, which is consistent with visual interpretation. The result shows that C-SVM, SVM and ANN are more suitable for sophisticated vegetation classification of hyperspectral data, and C-SVM is the best option.
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Kun Shang, Lifu Zhang, Yisong Xie, "Evaluation of hyperspectral classification methods based on FISS data", Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 80020L (8 December 2011); doi: 10.1117/12.902908; https://doi.org/10.1117/12.902908
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