Study Of Stratigraphic Identification And Extraction Techniques On The Basis Of Remote Sensing

Lithology identification is based on the characteristics of spatial information andremote sensing data spectrum to determine the basis of physical characteristics andoutput characteristics of the exposed surface rock.The rock spectrum is characterizedby the use of remote sensing methods for remote sensing lithology identification.Withthe development of remote sensing technology, and promote the study of rock spectralsignatures. In the spectral features of rocks on the basis of remote sensing lithologyrecognition technology has also been more in-depth developmentIn this paper,in Xinjiang Tuokexun Caihuagou Sulfur, copper, tungsten, zinc,silver metallic ore and theXinjiang Dabancheng Buogeda Copper as a study area.According to the geological characteristics of the mine and existing geological data,the typical lithology of the spectra and spectral analysis on the characteristics of thestudy area, the lithology interpreted to provide technical support and effective methodof prospecting. The methods used in this work and results obtained are as follows:(1) First in the work,on the ArcGIS platform, remote sensing images of the studyarea to establish a spatial grid database, provided a favorable basis and guarantee forthe follow-up of lithology feature extraction and lithology interpreted.(2) According to Tuokuxun Caihuagou Mining area of sericite-quartz schistspectral characteristics, Selected characteristics of ASTER band, Principal componentanalysis for spectral enhancement,The principal component images in the tone,Texture more clearly reflect the sericite-quartz schist in the study area distribution,Color composite image, you can clearly circle the sericite-quartz schist, lithologicboundaries.(3)Acording to Dabancheng Mining area,The spectral characteristics of a typicaldiabase,ASTER band selected characteristics, using the principal component analysis,principal component image in the tone, texture more clearly reflect the distribution ofdiabase in the study area; in the color composite image, you can clearly ring out of the diabaselithologic boundaries.(4) Choose the best band combination, unsupervised classification andsupervised classification of remote sensing images of the study area, the classificationresults with the existing geological data comparison, extraction lithologic informationin the study area.