The Research And Application Of Two New Approachs On Extracting Remote Sensing Information Of Mineralization And Alteration Rock

The western high-cold mountainous districts are the most important mineral-forming areas, with extremely high mineral exploration potential. Using Remote Sensing technique and combining with geology, geophysics and geochemistry technology, mineral exploration in high-cold mountainous areas is fast and efficient way. Altered rock's information extraction is the most important technique in Remote Sensing mineral exploration. However altered rock's information is weak in Remote Sensing image, sometimes the effect with traditional information extraction approachs is not very good. So we can see that it is considerably significant to explore new approach of mineral resources searching and evaluating in accordance with west's natural condition.Support Vector Machine(SVM) is a hotspot in machine learning field and has been successfully applied in different fields. Spectral Similarity Scale(SSS) is a new approach which can simultaneously measure the magnitude and direction of spectral vectors, and it can improve the veracity of similarity description among spectral vectors.Based on the subordinate subject of the brainstorm state projects of the "Tenth Five-year Plan", two new approachs-SVM and SSS in Remote Sensing altered rock's information extraction are discussed.Firstly, the paper introduces the present researching situation of Remote Sensing altered rock's information extraction. Secondly, it discusses the SVM basic theory, concept and key technical problems by using SVM and SSS, and then the paper systematically researches the new approachs in the application of Remote Sensing information extraction selecting several typical mineralization districts as testing areas. The following research work has been done:Experimental attestation is made to several key problems in the application of SVM using inborn Remote Sensing simulative images. Author researches how different kernel functions, kernel parameters, penalty parameters, multi-classifying algorithm and the number of training examples affect the performance of SVM classifier. The result...