Keywords: 
• forest mensuration /
• remote sensing /
• classification /
• hyperspectrum /
• endmember /
• texture

Abstract: Hyperspectral remote sensing has an obvious spectral signature，which can provide detailed spectral mapping across all 220 bands with high radiometric accuracy，but it is difficult to achieve classification accuracy for different cover types with their unique spectrum especially for the fine vegetation categories. To improve classification and to overcome lack of precision with the unique spectral signature of Hyperion hyperspectral imagery data, spectrum data together with textural information，which described the images change of gray scale and structural characteristics in the research area of Baizhang Town，Yuhang District， Hangzhou City，was used with endmembers of roads, buildings，farms，Phyllostachys edulis，Pinus massoniana，Quercus，and other species being extracted from images based on sub-compartmental division of high resolution images. Then，from these seven mixed endmembers，linear spectral unmixing was conducted. Next，the second order probability matrix from ENvironment for Visualizing Images （ENVI）software was used to extract eight texture quantities from the unmixing results. Finally，all texture quantities together with the eight unmixing endmembers were utilized for classification, and compared to treatments of Spectral Angle Mapper and replications of Support Vector Machine with single spectral information，precision of building increased 34.13% and 17.16%，accuracy of farming improved 19.71% and 9.24%，precision of Pinus massoniana increased 27.09% and 5.42%，accuracy of Quercusoak improved 3.00% and 10.00% nearly. Classification accuracy of most cover types increased. Therefore, to achieve classification of Hyperion hyperspectral imagery data with spectrum and textural information and to solve the salt and pepper effect problem，extraction of endmembers， combinations of eigenvectors during texture analysis, and selection of the texture sizes moving window，all played an important role during the classification process.［Ch，6 fig. 1 tab. 19 ref.］