Spectral Feature Extraction And Matching Resesarch On Hyper-Sectral Remote Sensing Image

The demand for mineral resources and energy has become greater and greater with China's sustainable and rapid economic development. However, the safeguard system of China's current major mineral resources is very grim. Remote sensing technology, as an effective means of earth observation, has gone through the full-color photography, color photography, multi-spectral remote sensing, and hyper-spectral remote sensing in different historical stages. Since 1970s, remote sensing technology was widely used in the field of land resources survey and monitoring, and has achieved fruitful results. Hyper-spectral remote sensing has greatly enhanced the ability of earth observation and discrimination of ground objects, has increased the quantitative level of remote sensing technology, has made the discrimination of ground objects develop into directly identify phase. And utilizing hyper-spectral to explore mineral resources is one of the main applications of remote sensing technology.At present, mineral discrimination and classification method of hyper-spectral image is divided into two kinds; one is mathematical statistics method, which is based on the mathematical transformation to reduce the dimension of the image, such as principal component analysis. Another is based on the mineral spectra's physical formation mechanism, directly use hyper-spectral data's feature of high spectral resolution, by selecting the absorption spectrum and calculating spectral absorption characteristics and other methods to discriminate the rock and mineral, such as the spectral feature fitting. Combining the two methods to discriminate of mineral classification effectively, starting from the physical formation mechanism of the mineral spectra, studying and analyzing the ground objects'diagnostic spectral characteristics, and using the theory of mathematical statistics methods to match features, identify, classify and mapping minerals precisely has been the focus of this thesis.Multi-feature matching decision tree for mineral identification and classification mapping technology is a process that involves knowledge discovery and expression, rule definition, establishment and operation of the decision tree. The existing different mineral identification trees only uses single features (such as the spectral main absorption peaks) and the same classification algorithm for mineral identification, or use different features while use the same algorithm, but the two decision trees are Limited to improve the accuracy of recognition to some extent. To improve the minerals'recognition accuracy, this thesis establishes a multi-variable decision tree according to the spectral absorption characteristics of various typical altered minerals. Firstly, this thesis calculates the main absorption features of the typical alteration minerals in the study area, and expresses these features in the form of knowledge. Then calculates the information entropy of the spectral features images, chooses spectral absorption index, absorption depth, spectral slope, left regional area, and other features according to the entropy, and realizes the definition of mineral identification rules by combining with minerals'main absorption features. Finally, builds and runs the decision classification tree based on the definition rules and gets the mapping results. The mapping results are basically consistent to Huang Dinghua et al from China University of Geosciences (Wuhan) and that of Remote Sensing Center of China Aviation Land and Resources, especially epidote and serpentine mapping results. As for the muscovite, chlorite, calcite minerals, the three mapping results are slightly different. This shows that the method of knowledge representation and rules definition for mineral mapping on simulated data is feasible and effective, and to a certain extent, it can achieve mapping of the mineral type and distribution. It also illustrates the established mixed-decision classification tree has a strong classification ability. In order to evaluate the classification accuracy quantitatively of the decision tree, this thesis applies the multi-feature decision tree and SAM method on simulated images which is randomly generated on the USGS spectral library the results shows that recognition accuracy of the multi-feature decision tree was 89.06%, while the SAM method is 79.99%. This represents that the decision tree can still be effective and keeps good robustness with the absence of the prior knowledge of the study area.However, the research results are mainly focused on the common typical alteration minerals, so how to expand to research methods on other types of alteration minerals and improve the accuracy of identification will be the focus of future research.