Remote sensing images show a very promising perspective for distinguishing tree species,especially those with the very high resolution ranging from 1 to 4 m.However,the traditional methodology for classifying land cov...Remote sensing images show a very promising perspective for distinguishing tree species,especially those with the very high resolution ranging from 1 to 4 m.However,the traditional methodology for classifying land cover types,solely depending on spectral features,while texture and other spatial information are neglected, has the weakness such as inadequately utilization of information,low accuracies of classification,etc. Considering to the texture differences among forest species,it is more important for spatial information description of high-resolution remote sensing image to improve the precision of textural features choosing.In this study,the factors to influence the nine textural features choosing were analyzed and the results showed that the moving window size was the main factor to affect the obtaining processes of textural features based on the gray level co-occurrence matrix(GLCM) method,and the imagery was then classified combining the maximum likelihood classification(MLC) method with the original spectral values and texture features.First,this study utilized a correlation analysis of the images from a principal component analysis.Second,through multiple information sources,including textual features derived from the data.For the high-resolution remote sensing image, the most proper moving window size was determined from 3×3 to 31×31.Classification of the major tree species throughout the study area (the SunYat-Sen Mausoleum in Nanjing) was undertaken using the MLC.Third,to aid forest research,classification accuracy was improved using the GLCM.According to correlations among textures and richness of the data,GLCM provided the best window size and textural parameters. Results indicated that the texture characteristics were add in the spectral characteristics to improve the precision of the results of the classification, 19×19 window for best window.The total precision can reach 66.322 6%,Kappa coefficient is 0.584 0.Each tree species has greatly improved accuracies of the classification.By the calc展开更多
CKD_(0A)型机车装用美国CATERPILLAR公司C32型柴油机,装车功率830 k W,采用德国KNORR符合UIC标准的微机控制的电空制动系统,带有电阻制动及重联控制功能,具有满足地铁限界、牵引力大、曲线通过能力强等特点。对机车的总体布置、参数、...CKD_(0A)型机车装用美国CATERPILLAR公司C32型柴油机,装车功率830 k W,采用德国KNORR符合UIC标准的微机控制的电空制动系统,带有电阻制动及重联控制功能,具有满足地铁限界、牵引力大、曲线通过能力强等特点。对机车的总体布置、参数、各子系统功能和主要技术特点进行了介绍。展开更多
文摘Remote sensing images show a very promising perspective for distinguishing tree species,especially those with the very high resolution ranging from 1 to 4 m.However,the traditional methodology for classifying land cover types,solely depending on spectral features,while texture and other spatial information are neglected, has the weakness such as inadequately utilization of information,low accuracies of classification,etc. Considering to the texture differences among forest species,it is more important for spatial information description of high-resolution remote sensing image to improve the precision of textural features choosing.In this study,the factors to influence the nine textural features choosing were analyzed and the results showed that the moving window size was the main factor to affect the obtaining processes of textural features based on the gray level co-occurrence matrix(GLCM) method,and the imagery was then classified combining the maximum likelihood classification(MLC) method with the original spectral values and texture features.First,this study utilized a correlation analysis of the images from a principal component analysis.Second,through multiple information sources,including textual features derived from the data.For the high-resolution remote sensing image, the most proper moving window size was determined from 3×3 to 31×31.Classification of the major tree species throughout the study area (the SunYat-Sen Mausoleum in Nanjing) was undertaken using the MLC.Third,to aid forest research,classification accuracy was improved using the GLCM.According to correlations among textures and richness of the data,GLCM provided the best window size and textural parameters. Results indicated that the texture characteristics were add in the spectral characteristics to improve the precision of the results of the classification, 19×19 window for best window.The total precision can reach 66.322 6%,Kappa coefficient is 0.584 0.Each tree species has greatly improved accuracies of the classification.By the calc
