期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B7
页码：1181-1186
出版社：Copernicus Publications
摘要：Statistical analysis is a widely used traditional technique for classification and discrimination in remotely sensed images, which supposes that there is only one endmember in an image pixel. However, the fact is that the ground sampling distance is generally larger than the size of targets of interest. So the statistical analysis technique is not suitable. In this case, classification and discrimination must be carried out at subpixel level. In this paper the abundance fractions of endmembers in an image pixel are estimated by UFCLS (unsupervised fully constrained least squares) method based on the inversion of linear spectral mixture model. This method allows us to extract necessary endmember information from an unknown and no prior knowledge image scene so that the endmembers present in the image can be quantified. The pixel classification generates a gray scale image, whose gray level values are determined by the estimated abundance fractions of endmembers. The band expansion technique is used to create additional bands from existing multispectral bands using band-to-band nonlinear correlation. These expanded bands ease the problem of insufficient bands in TM imagery. In the two experiments, the results of the pixel classification show that the effects are good. The pixel classification image of vegetation agrees significantly with the NDVI image, but the contrast of the former is a little larger than that of the latter, so there was lack of the information of details and edges. However, compared to color composite image of raw bands 4, 3 and 2 in red, green and blue respectively, especially in the second experiment, the results of vegetation classification are excellent. The shade areas in the first experiment are not classified correctly. Compared to CSMA (constrained spectral mixture analysis) method, UFCLS method is better in both the effects of classification and the consumption of computation time
关键词：classification of remote sensing image; linear spectral mixture model; fully constrained least squares (FCLS) ; algorithm; unsupervised FCLS (UFCLS) method; constrained optimization problem
