The classification of thematic mapper imagery in areas with strong topographic variations has proven problematic in the past using a single classifier, due to the changing sun illumination geometry. This often results...The classification of thematic mapper imagery in areas with strong topographic variations has proven problematic in the past using a single classifier, due to the changing sun illumination geometry. This often results in the phenomena of identical object with dissimilar spectrum and different objects with similar spectrum. In this paper, an integrated classification method that combines a decision tree with slope data, tasseled cap transformation indices and maximum likelihood classifier is introduced, to find an optimal classification method for thematic mapper imagery of plain and highland terrains. A Landsat 7 ETM+ image acquired over Hangzhou Bay, in eastern China was used to test the method. The results indicate that the performance of the inte- grated classifier is acceptably good in comparison with that of the existing most widely used maximum likelihood classifier. The integrated classifier depends on hypsography (variation in topography) and the characteristics of ground truth objects (plant and soil). It can greatly reduce the influence of the homogeneous spectrum caused by topographic variation. This integrated classifier might potentially be one of the most accurate classifiers and valuable tool for land cover and land use mapping of plain and highland terrains.展开更多
According to the data characteristics of Landsat thematic mapper(TM) and MODIS,a new fusion algorithm about thermal infrared data has been proposed in the article based on improving wavelet reconstruction.Under the do...According to the data characteristics of Landsat thematic mapper(TM) and MODIS,a new fusion algorithm about thermal infrared data has been proposed in the article based on improving wavelet reconstruction.Under the domain of neighborhood wavelet reconstruction,data of TM and MODIS are divided into three layers using wavelet decomposition.The texture infonnation of TM data is retained by fusing high-frequency information.The neighborhood correction coefficient method(NCCM) is set up based on the search neighborhood of a certain size to fuse low-frequency information.Thermal infrared value of MODIS data is reduced to the space value of TM data by applying NCCM.The data with high spectrum,high spatial and high temporal resolution,are obtained through the algorithm in the paper.Verification results show that the texture information of TM data and high spectral information of MODIS data could be preserved well by the fusion algorithm.This article could provide technical support for high precision and fast extraction of the surface environment parameters.展开更多
文摘The classification of thematic mapper imagery in areas with strong topographic variations has proven problematic in the past using a single classifier, due to the changing sun illumination geometry. This often results in the phenomena of identical object with dissimilar spectrum and different objects with similar spectrum. In this paper, an integrated classification method that combines a decision tree with slope data, tasseled cap transformation indices and maximum likelihood classifier is introduced, to find an optimal classification method for thematic mapper imagery of plain and highland terrains. A Landsat 7 ETM+ image acquired over Hangzhou Bay, in eastern China was used to test the method. The results indicate that the performance of the inte- grated classifier is acceptably good in comparison with that of the existing most widely used maximum likelihood classifier. The integrated classifier depends on hypsography (variation in topography) and the characteristics of ground truth objects (plant and soil). It can greatly reduce the influence of the homogeneous spectrum caused by topographic variation. This integrated classifier might potentially be one of the most accurate classifiers and valuable tool for land cover and land use mapping of plain and highland terrains.
基金Supported by the National Natural Science Foundation of China(No.41101503)the National Social Science Foundation of China(No.11&ZD161)Graduate Innovative Scientific Research Project of Chongqing Technology and Business University(No.yjscxx2014-052-29)
文摘According to the data characteristics of Landsat thematic mapper(TM) and MODIS,a new fusion algorithm about thermal infrared data has been proposed in the article based on improving wavelet reconstruction.Under the domain of neighborhood wavelet reconstruction,data of TM and MODIS are divided into three layers using wavelet decomposition.The texture infonnation of TM data is retained by fusing high-frequency information.The neighborhood correction coefficient method(NCCM) is set up based on the search neighborhood of a certain size to fuse low-frequency information.Thermal infrared value of MODIS data is reduced to the space value of TM data by applying NCCM.The data with high spectrum,high spatial and high temporal resolution,are obtained through the algorithm in the paper.Verification results show that the texture information of TM data and high spectral information of MODIS data could be preserved well by the fusion algorithm.This article could provide technical support for high precision and fast extraction of the surface environment parameters.
