Mapping of mineral deposits using image fusion by PCA approach

Earth Observation using Satellite imagery is a challenging tool for analysis but is proved an effective tool in offering a wide coverage. Satellite Imagery is an important economical tool in accessing mineral exploration. Mineral mapping using satellite image processing is a large scale approach to exploit available minerals in the earth's crust. There are various satellite sensors to imply the presence of minerals. Each sensor has its own characteristics. Data fusion is the method of collecting and combining data from multiple sensors. In Geographic information Systems, Various data are used for spatial decision making. The relation between different set of data can be denoted in matrix format and its properties are used by analysing various algorithms. Image fusion is used for combining the significant features which are captured by various image sensors. Image sharpening, feature enhancement and image classification can be established using Image Fusion algorithms. Image fusion can be applied at various levels like decision, feature, and pixel level. Mineral exploration is done on the decision and feature level in GIS. In this paper, Principal component analysis method (PCA) was used for combining multi-source and multi-scale geo-information at pixel level for Hyperion and ALI data. Hybrid image is obtained by combining the values of pixels which is spatially based for different set of images and thus generated image is used for extracting the information or classification. Results obtained in this paper give the distribution which is of spatially based for mineral deposits in the study region with the help of AO-1 Hyperion and ALI data.