Evaluation of hyperspectral classification methods based on FISS data
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 images could be considered as pure pixels with high spatial and spectral resolution, which makes FISS a perfect option on the study of classification 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 saved. 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.
Fiss Data Hyperspectral Image Classification Composite Kernel Support Vector Machine Artificial Neural Net Spectral Angle Mapping Maximum Likelihood
Kun Shang Xia Zhang Lifu Zhang Yisong Xie
The State Key Laboratory of Remote Sensing Sciences, Institute of Remote Sensing Application,Chinese The State Key Laboratory of Remote Sensing Sciences, Institute of Remote Sensing Application,Chinese
国际会议
桂林
英文
1-8
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）