This paper is an attempt to introduce the role of earth observation technology and a type of digital earth processing in mineral resources exploration and assessment. The sub-pixel distribution and quantity of alteration minerals were mapped using linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) algorithms in the Sarduiyeh area, SE Kerman, Iran, using the visible-near infrared (VNIR) and short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument and the results were compared to evaluate the efficiency of methods. Three groups of alteration minerals were identified: (1) pyrophylite-alunite (2) sericite-kaolinite, and (3) chlorite-calcite-epidote. Results showed that high abundances within pixels were successfully corresponded to the alteration zones. In addition, a number of unreported altered areas were identified. Field observations and X-ray diffraction (XRD) analysis of field samples confirmed the dominant mineral phases identified remotely. Results of LSU and MTMF were generally similar with overall accuracy of 82.9 and 90.24%, respectively. It is concluded that LSU and MTMF are suitable for sub-pixel mapping of alteration minerals and when the purpose is identification of particular targets, rather than all the elements in the scene, the MTMF algorithm could be proposed.

This paper focuses on mapping jarosite and different types of alteration minerals for mineral exploration, particularly porphyry copper deposits and discriminating alteration zones with high-potential mineralization from those showing low potentials. The study area is situated at the Central Iranian Volcano-Sedimentary Complex, where the large copper deposits like Sarcheshmeh as well as numerous occurrences of copper exist. The visible near infrared and shortwave infrared (VNIR-SWIR) bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used for mineral mapping. The spectra of diagnostic alteration mineral groups, including sericite-illite, pyrophyllite-alunite, kaolinite-dickite, chlorite-calcite-epidote, and jarosite were extracted from VNIR-SWIR bands of the ASTER imagery. These spectra were used for mineral identification through mixture tuned matched filtering (MTMF) algorithm. Results showed that alteration minerals, and the areas containing jarosite were discriminated from the surrounding districts, which illustrate the ASTER ability to provide information on the occurrence of these minerals. Identification of these areas is valuable for mineral exploration, discrimination of strong pyritization, gossans, and the mine tailings. Results also support the role of MTMF as an effective image processing technique for mineral mapping and exploration.