In this study a new algorithm to detect hidden lineaments in the satellite images is presented. The study area is located in the south to southeast of the Sarcheshmeh deposite, which include major deposits of porphyry copper deposits such as Darrehzar, Kouh panj, Dehsiahan(Bandar Bagho), Saridoun Deposits and some known mineral indications such as Mamzar, Parsian, Hossein Abad, Siah Khan Khavari and Kouh panj indices. Existing faults and fractures make satellite image of this area suitable for testing the algorithm. For this purpose the ASTER image is used. In image processing for edge and lineament detection mostly edge detection operators were employed. Canny operator is the widely used in this field. This operator also has been used in various fields such as medical imaging, robotics and remote sensing. In this research a number of automatic and semi-automatic algorithms were presented that each has advantages and disadvantages. The first step is extracting lineaments from the set of edges. for this purpose Radon transform and Hough transform have been implemented in MATLAB. To improve the accuracy of the edges some tools have been used in order to contrast enhancement, appropriate image pre-processing, supervised classification and color space conversion. These color spaces to extract lineaments have been studied: RGB, XYZ, HIS, YCbCr, CIE L*u*v*. The results of using color spaces and supervised classification are compared. It was observed that the HSI color space, followed by L*u*v* color space specify more details from lineaments. Appropriate preprocessing of data has a significant impact on improving results. The most important preprocessing are histogram equalization, and binarization with Otsu threshold. The algorithm has been tested with different images as inputs. These input images are classified three categories: 1) False Color Composite 2) Supervise Classification 3) Output of Color Spaces. The best result obtained is a combination of the results from different inputs. Due to the fact that the second derivative of the magnetic data highlight faults and fractures, the magnetic data of region has also been used, in order to extract the lineaments. To quantify the results, appropriate buffering was considered first, then the percentage of match between detected faults and faults of geological map was obtained. The final results show acceptable agreement between detected and observed faults within and one observed in geological map.
