Due to the extent of mineral deposits, identification and proper management of resources is very important. According to the advent of remote sensing and specially producing hyperspectral remote sensing data which can get abundant spectral information, using this data for detailed study is rapidly expanding. Launch of the EO-1 in November 2000 introduced hyperspectral sensing of the earth from space through the Hyperion system. Hyperion has a single telescope and two spectrometers in visible near-infrared (VNIR) and short-wave infrared (SWIR). These spectral bands could provide abundant information about many important earth-surface minerals. Therefore one of the main aim of the present study was to examine the feasibility of the EO-1 Hyperion data in discriminating and mapping alteration zones around porphyry copper deposits (PCDs). 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 data were used for image classifying and specially alteration mapping. The Pre-processing which was implemented on the level 1R Hyperion data in order to remove noise and acquire surface reflectance includes five steps that named removing uncalibrated bands, spatial displacement correction, destriping, spectral curvature (smile) correction and at last atmospheric correction. It is noticeable that atmospheric correction, because of using the target detection algorithm, SAM, is one of the most important step in this study. Therefore the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm, available in ENVI software, was implemented to obtain surface reflectance data. This algorithm is a MODTRAN4-based atmospheric correction software package designed to eliminate atmospheric effects through derivation of atmospheric properties such as surface albedo, surface altitude, water vapor column, aerosol, and cloud optical depths, as well as surface and atmospheric temperatures from hyperspectral data. In this paper Spectral Angle Mapper (SAM) and Gaussian Mixture Model (GMM) were implemented on pre-processed and calibrated Hyperion dataset. For using SAM algorithm, introducing reference spectra is obligatory. Information extraction from a Hyperion data set involves several processes including extraction of scene spectral endmembers using an integration of MNF, pixel purity index (PPI), and n-dimensional visualizer approaches. Then the extracted spectra which characterized using spectral analysis procedure available at ENVI and visual inspection, were used as reference for subsequent processing by SAM algorithm. On the other hand Gaussian mixture model (GMM) has been successfully used for HSI classification. It has also proved beneficial for a variety of classification tasks, such as speech and speaker recognition, clustering, etc. For estimating the parameters of GMM, the Expectation-Maximization (EM) algorithm was used. In order to compare and assess the accuracy of methods proposed in this study, a simulated data used to demonstrate the efficiency of algorithms which used in this study. Results revealed that Hyperion data prove to be powerful in discriminating and mapping various types of alteration zones while the data were subjected to adequate pre-processing. Overall accuracy and kappa coefficient for results of SAM and GMM are 82%, 0.75 and 80%, 0.71 respectively.

This study was implemented to prepare a model for soil salinity mapping using Landsat5 images in several provinces including Bushehr, Semnan, Fars, Kerman and Hormozgan. At the beginning, 50-100 samples from soil surface were taken and sent to the Laboratory. Then in order to evaluate and identify soil salinity, TM Landsat satellite images and statistical models combined with satellite`s spectral indices were used. After evaluating the accuracy of statistical models using test points, the best model for the study area was selected and the salinity maps were developed based on the regression model. The results showed a significant relationship between soil salinity and spectral index. In Fars province, NDSI salinity index had the highest correlation with soil electrical conductivity (0. 35) with a regression coefficient of 66% and RMSE and MBE statistics of 2. 58 and 0. 66, respectively. In Kerman province, the tasseled cap three index had the highest correlation with soil electrical conductivity (0. 47) with a regression method coefficient of 65%, and RMSE and MBE of 10. 3 and 0. 51, respectively. In Hormozgan province, the results showed high correlation with soil salinity indicators SI2 level of 72 percent. Stepwise method with R-square of 0. 518 was selected for the Hormozgan province whre the RMSE and MBE were reported to be 2. 5 and-0. 35, respectively. Also in Semnan province, 5 and 7 bands of Landsat showed the highest correlation with soil electrical conductivity (respectively 0. 65 and 0. 75). By using stepwise regression, the linear relation with R-square of 0. 6 was obtained, and RMSE and MBE values were reported to be 2. 83 and-0. 81, respectively.

The mixed pixels influence the overall accuracy of land cover maps produced by using the remote sensing images with different spatial resolutions. In recent years, soft-then-hard super resolution mapping (STHSRM) has been proposed to solve the problem of mixed pixels. This method estimates soft attribute values for land cover classes at the subpixel scale level and then allocates classes for subpixels according to the soft attribute values. Subpixel/pixel spatial attraction model (SPSAM) calculates soft attribute values for each class at fine pixels by spatial attraction between subpixels and their neighboring pixels. UOC (Units of Class) allocates classes to subpixels in units of class. First, a visiting order for all classes is predetermined. Then, according to the visiting order, the subpixels belonging to the being visited class are determined by comparing the soft attribute values of this class. The remaining subpixels are used for the allocation of the next class. This paper proposed a new spatial-spectral attraction model to estimate the soft attribute value for each class at each subpixel. Also it presents a novel class allocation approach based on UOC technique for STHSRM algorithm. The proposed class allocation approach produces the optimal location of subpixels by defining the cost function and calculating the corresponding cost of spatial arrangement of sub-pixels in different visiting order of classes. The technique is applied to Worldview-3 and ROSIS-03 images. A comparison between the results obtained through the proposed approach and an existing super-resolution mapping technique is introduced. The results show that the proposed algorithm is able to produce higher SRM accuracy than the other approaches especially in linear feature and class boundaries. The improvement value of the adjusted Kappa coefficient of the proposed algorithm related to the spatial attraction model with the AUOC class allocation technique in the scale factor 2, is 0. 053 and 0. 032, respectively.

Hyper spectral satellite imagery like Hyperion implies collecting data from large number of spectral bands. Therefore, they can be used for some applications that cannot be executed by multispectral satellite images. The advantage of higher spectral resolution is accompanied by the weakness of lower spatial resolution for some applications. Consequently, we are always facing with mixed pixels, i.e. mixed, or a mixture of spectral responses of background and several objects (Fauvel et al., 2006). Sub-pixel target detection methods are able to identify the percentage and location of objects in a mixed pixel.

Exploration of the new mineral deposits around the existing mines is an important objective in mining industry. Using multispectral remote sensing images, due to their diversity and vast availability is a useful tool to meet this purpose. In this research, the Zonouz region was investigated for discovering new high potential kaolinite mineralization areas using Landsat8 and ASTER data. Zonouz kaolin mine which is located in Marand county, East-Azarbaijan, is the biggest kaolin deposit in the Middle-East. In the current research, the capability of Landsat8, as a new generation multispectral data, and ASTER data were examined in mineral detection. At first, the preprocessing of data, i. e. atmospheric and topographic corrections and elimination of the vegetation cover were carried out. Then, the spectral profiles of the endmembers of the study area datasets were extracted. Identification of the extracted endmembers was done by comparison of the unknown spectra with reference spectra of the USGS spectral library, and 3 minerals including kaolinite, quartz, and Fe-bearing minerals were identified. Finally, the distribution maps of the identified minerals were extracted by using of the artificial neural networks, as a non-linear supervised method. To the best of our knowledge, the applied neural networks structure has not been implemented on LANDSAT 8 and ASTER data earlier. This research is also the first implementation of LANDSAT 8 and ASTER data in Zonouz kaolin region for extension mapping of the kaolin. Two different approaches including virtual verification and field sampling were applied for validation of the results. According to the Findings of this research, both of ASTER and Landsat8 datasets proved successfulness for identifying the kaolinite; but the Landsat 8 data exhibited better performance in detecting and mapping of the quartz and hematite. Finally, 6 promising areas were determined as high potential zones of kaolinite mineralization for future studies.

Fire severity mapping is very important for managing the fires in forest ecosystems. The extraction of spectral indices from optical sensors is recognized as one of the most effective bands for the classification of vegetation classes. In this study, the ability and sensitivity of some spectral indices extracted from Sentinel-2 and Landsat 8-OLI images with different spatial resolutions have been investigated for fire severity mapping using the Random Forest algorithm in a burned area located in the reforested area of Arabdagh, Golestan province. After necessary preprocessing on the bands, the appropriate mono and bi-temporal spectral vegetation indices were created. The optimal index values for bands in the bi-spectral spaces pre/post-fire were calculated to evaluate the sensitivity of bands to the changes occurring within the fire classes. The best results were obtained for the NIR-SWIR2 bands with an optimal index value of 0. 77 for Sentinel-2 and 0. 67 for Landsat8-OLI. The best indices were selected based on values of optimality index. The values of these indices were calculated after the fire as well as the differential (pre/post-fire) ones. The ground truth of fire severity classes map was prepared by a selective sampling method through field surveying. The classification was done with different indices by random forest (RF) algorithm and the results were assessed by the ground truth points. The result showed that the best results were obtained for a combination of many differential indices from all bi-bands of Landsat 8-OLI with kappa coefficient (0. 96).

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.

Soil salinity undergoes significant spatial and temporal variations,therefore, salinity mapping is difficult, expensive, and time consuming. However, researchers have mainly focused on arid soils (bare) and less attention has been paid to halophyte plants and their role as salinity indicators. Accordingly, this paper aimed to investigate the relationship between soil properties, such as electrical conductivity of the saturation extract (ECe) and the spectral reflectance of vegetation species and bare soil, to offer a method for providing salinity map using remote sensing. Various vegetation species and bare soil reflectance were measured. Spectral Response Index (SRI) for bare soil and soil with vegetation was measured via the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and salinity indexes. The electrical conductivity of the saturated extract, texture, and organic matter of soil samples were determined. The correlation coefficient of soil salinity with SRI, SAVI, and salinity indexes were obtained, and a model was presented for soil salinity prediction. EC map was estimated using the proposed model. The correlation between SRI and EC was higher than other models (0. 97). The results showed that the salinity map obtained from the model had the highest compliance (0. 96) with field findings. In general, in this area and similar areas, the SRI index is an acceptable indicator of salinity and soil salinity mapping.