Remote sensing technology has recently been used in various categories, and is considered as an efficient method in lithological mapping. The ASTER data, in this regard, have been vastly used in mineral and rock enhancement. The objective of this research is comparing the spectral angle mapping and spectral feature fitting algorithms in enhancing the Neyriz ophiolite lithological units based on the calibrated SWIR and TIR data of ASTER. The Neyriz ophiolite (53° 52' 30"–54° 14' 05" E, and 29° 15' 26" – 29° 40' 22" N) is one of the several large Tethyan ophiolites in a 3000 km abduction belt that was thrust over the edge of the Arabian continent during the Late Cretaceous (Alavi, 1994). Two geological maps-both at scale of 1: 100, 000- were compiled and published by the Geological Survey of Iran (1994, 1996) for the study area. A generalized geological map and the field photographs of the main lithological units are shown in figures 1 and 2. These evidences were used for comparing the output images to the field criteria. The geological maps were also applied as references for accuracy assessment of output results. Rock units of the study area occur at four geological zones, including: 1) Sanandaj- Sirjan, 2) Tertiary flysch, 3) Ophiolitic zone, and 4) Zone of Pichakan radiolarite, from NE to SW. A total of 50 collected samples were analyzed spectrally in the laboratory of Bowling Green State University, USA, using Analytical Spectral Device (ASD) with spectral range of 0.4– 2.5 mm, and Fourier Transform Infrared spectrometry (FTIR) with spectral range of 6-16 m. The high resolution spectra obtained from these instruments were then resample to the ASTER 9 VNIR-SWIR (figure 3) and 5 TIR (figure 4) bands of ASTER in order to determining the diagnostic absorption features of each rock unit being used as an input to surface lithology mapping in SAM and SFF algorithms. As described by Hunt and Salisbury (1970), Burns (1970), Hunt et al. (1974), Adams, (1974), Hunt and Ashley (1979), Hunt and Evarts (1980), King and Ridley (1987), Vander Meer et al. (1997), Vincent (1997) both the fresh and weathered surfaces of igneous rocks show strong absorptions in the visible-near infrared region of the spectrum due to the presence of iron. Serpentines peridotites have multiple absorption bands near 1.4mm and 2.3 mm, with supplementary broader and weaker features near 1.95 mm and 2.1 mm. These features can be attributed to vibration overtone and combination tones involving OH-stretching modes. Gabbros display broad absorptions typical of ferrous ion, centered near 1.28 and 1.85 mm. Diabases show strong features near 2.3 mm that could be attributed to Mg-OH vibration in epidotic. Absorption features of the radiolarian charts are dominant near 0.48, 0.9, 2.2 and 2.45 mm. Because of combination and overtone bands of the CO3 fundamentals occurring in marbles, they display absorption bands near 1.87, 1.99, 2.15 and 2.33 mm. A cloud-free day-time ASTER level 1B scene, acquired on 8th of September 2003 and subsets corresponding to the Neyriz ophiolite zone were extracted from them. The Atmospheric and Topographic Correction (ATCOR) and Reference Channel (available in ENVI 4.4) models were carried out on the VNIR-SWIR and TIR datasets, respectively. The ophiolite rock units were mapped by using the Spectral Angle Mapping (SAM) and Spectral Feature Fitting (SFF) techniques implemented on the calibrated datasets using field samples spectra (figure 5 and 6). The output results were validated by the use of field observations and geological map evidences as well as using a confusion matrix and Kappa Coefficient. The overall accuracy and Kappa Coefficients obtained from SFF and SAM algorithms, based on calibrated SWIR data, are 0.88, 90% and 0.76, 80%, respectively. Comparing the results of these algorithms with geological map and field observations and the results obtained by confusion matrix showed that because of the continuum removal and the resulting normalized spectral features, spectral feature fitting has more accuracy in enhancing lithological units based on the SWIR dataset. This algorithm could enhance lithological unit’s harzburgite-lherzolite, gabbros, marble, harzburgite- dunite, database and radiolarite without enhancing the surrounding exposures. However, exposures such as lake and alluvial sediments, screed and agricultural lands were co-enhanced with these rock units while using the SAM algorithm. Results also showed that the spectral features of rock units exposed at the area are dominantly located at the shortwave infrared (SWIR) region, so this dataset could enhance lithological units better than TIR.