This study has tried to prove the ability of remote sensing techniques to extract information necessary for preparation of geological mapping of the earth’s surface using multi-spectral satellite images which are ric...This study has tried to prove the ability of remote sensing techniques to extract information necessary for preparation of geological mapping of the earth’s surface using multi-spectral satellite images which are rich sources of Earth’s surface information. In this study, the surface geological mappings of Zefreh region have been investigated through ASTER, OLI, and IRS-PAN remote sensing data. To prepare the geological map, preprocessing steps and reducing noises from data using MNF algorithm were firstly carried out. Then a set of processing algorithms and image classification methods are included;the band rationing, color composite and pixel classification based on maximum likelihood, spectral and sub-pixel classification methods of spectral angle mapper (SAM), spectral feature fitting (SFF), linear spectral differentiation (LSU), hill-shade images and automatic lineament extraction were used. Confusion matrix was formed for all classified images through control points were randomly selected from 1:25,000 map of the region to determine the accuracy of obtained results, which indicated the maximum accuracy (up to 90%) of output images. Comparing the results obtained from these methods with the map prepared by ground operations confirmed accuracy results. Finally, the surface geology and fault map of Zafreh region was produced by combining detected geological formations and tectonic lineaments.展开更多
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
文摘This study has tried to prove the ability of remote sensing techniques to extract information necessary for preparation of geological mapping of the earth’s surface using multi-spectral satellite images which are rich sources of Earth’s surface information. In this study, the surface geological mappings of Zefreh region have been investigated through ASTER, OLI, and IRS-PAN remote sensing data. To prepare the geological map, preprocessing steps and reducing noises from data using MNF algorithm were firstly carried out. Then a set of processing algorithms and image classification methods are included;the band rationing, color composite and pixel classification based on maximum likelihood, spectral and sub-pixel classification methods of spectral angle mapper (SAM), spectral feature fitting (SFF), linear spectral differentiation (LSU), hill-shade images and automatic lineament extraction were used. Confusion matrix was formed for all classified images through control points were randomly selected from 1:25,000 map of the region to determine the accuracy of obtained results, which indicated the maximum accuracy (up to 90%) of output images. Comparing the results obtained from these methods with the map prepared by ground operations confirmed accuracy results. Finally, the surface geology and fault map of Zafreh region was produced by combining detected geological formations and tectonic lineaments.
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
