capability assessment for RADAR and OPTIC data fusion and integrating methods to identify alteration areas

Optical Remote Sensing is a low-cost and efficient method to alteration zone detection. However in the area that have been covered by vegetation or alluvial, the identification of these areas is not very accurate with optical images. In this study fusion and integrating of ALOS-PALSAR L-band and ASTER data by HSV, HSL, Maximum Likelihood and Artificial Neural Network has been done to discover and enhance the Argilic and Propylitic Alteration zones over the west part of Qazvin province in IRAN. For this purpose, Argilic and Propylitic alterations were primary identified unseeing ASTER image. Then based on geological data and field study, some areas with alterations covered by quaternary sediments, not detectable by ASTER images, were identified. In the following, the integration of the ALOS PALSAR L-band data and the ASTER SWIR bands with HSV, HLS, Maximum Likelihood and Artificial Neural Network were performed. The results of this study showed that the radar and optics data fusion, using HSV and HLS methods, increases the enhancement of visible argillic alteration zones in the study area. Also, the integration of radar and optics data with the Maximum Likelihood and the Artificial Neural Network methods.