Study On Classification Of Forest Types Using Multi-angle And Hyperspectral Remote Sensing

Multi-angle hyper-spectral remote sensing had been applied successfully in various subjects, and gained some research achievements. The application potential had been showed. In the field of forestry, research works such as LAI, biomass, vegetation biochemical information, coniferous trees identification and so on are under way abroad. Application research of multi-angle hyper-spectral remote sensing in forestry had been carried continuously recent years in China too.In this paper, Changbai Mountain is selected as study area. According to the characteristic of CHRIS/PROBA multi-angle hyper-spectral remote sensing data, several common remote sensing image processing methods such as Maximum Likelihood Method (MLM), Minimum Distance Method (MDM), Support Vector Machine (SVM) and Spectrum Angle Mapping (SAM) are applied to classify forest types in CHRIS 0-degree image based on pre-processing. Classification method with relatively higher precision is acquired by results comparison. Then the method is applied to classify five angle images of CHRIS and the results are compared and analyzed. Afterward, the method is applied to classify multi-angle combination images and the results are compared and analyzed. Finally, the method is applied to classify band combination images and the results are compared and analyzed.Main study conclusion:By comparing and analyzing classification results of different method to CHRIS 0-degree image, SVM has the highest classification precision of 72.8448%, while the Kappa index is 0.6770. By comparing and analyzing classification results of SVM to CHRIS 5 angle images, the classification precisions are sorted as follow: FZA=0>FZA=-36>FZA=-55>FZA=36>FZA=55. By comparing and analyzing the classification results of SVM to multi-angle combination images with CHRIS 0-degree image, the overall tree type classification precision of multi-angle combination is lower than single-angle image. By comparing and analyzing the classification results of SVM to band combination images with CHRIS 0-degree image, the overall tree type classification precision of band combination is very low which is lower than multi-angle combination image classification. It is concluded that, application SVM to CHRIS single-angle image is the best method to classify forest type by CHRIS multi-angle hyper-spectral data in Changbai Mountain natural reserve.