Internal Average Relative Reflectance Research Articles

Using the band ratio approach, FULL-PIXEL (SAM and SFF) and SUB-PIXEL (MF and SID) methodologies to discover areas with alteration using the Aster sensor: a case study in north westen Iran - Sivardaghi Area

ASTER sensor data is among the most potent satellite data accessible for doing geological investigations, with images for the whole earth's surface. In order to test the capability of this sensor to detect places with geochemical alterations, photographs of Mount Seiver Daghi in the western Iranian province of Samal were utilized in this study. This region, which comprises of magmatic and volcanic terrain, is part of the Arsbaran territory and is covered by intrusive masses with alluvial and sedimentary deposits. To conduct this study, an ASTER measuring frame was utilized, which, after performing atmospheric corrections using the internal average relative reflectance (IARR) method of false color composite images and principal component analysis (PCA), was able to differentiate between different lithological units using the Band assignment method, full-pixel methods of spectral angle mapper (SAM) and base spectrum algorithm of spectral feature fitting (SFF) as well as sub-pixel methods of matched filtering. The study demonstrates that the approach of principal component analysis and false color composition is efficient for distinguishing sedimentary rock units from igneous rock units, and its application is suggested for the designated rock units. Due to the lack of spectral characteristics of feldspars and quartz in the short infrared wavelength range, the basic spectrum methods utilized in this work are incapable of identifying such minerals. It is not advised to use these algorithms to distinguish between various magmatic units.

A New Band Ratio Approach for Discriminating Calcite and Dolomite by ASTER Imagery in Arid and Semiarid Regions

In this article, a new approach is developed for discrimination of calcite (limestone) and dolomite (dolostone) by utilizing data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The study area is located at the Zagros Mountains, SW Iran, in which outcrops of sedimentary carbonate rocks are abundant. The approach exploits short-wave infrared spectral features of dolomite and calcite and is based on the spectral variations of these minerals in bands 7 and 8 of ASTER imagery. In order to evaluate the efficacy of the method, spectral analysis was carried out on the spectra of dolomite, dolostone, calcite, and limestone, available in ASTER spectral library. The statistical analysis of the spectra showed that dolomite and calcite have distinguishable b8/b7 band ratios. Spectral measurements of rock samples and knowledge of the mineral composition of rock samples attained by staining carbonate rocks in Alizarin red-S solution were used to evaluate the method used in this study. The applied imagery was normalized by the internal average relative reflectance (IARR) method due to the arid and semiarid climate of the region. The results showed that combined with spectral measurements of rock samples, the ((b5 + b9)/b7)/((b6 + b9)/b8), and b8/b7 band ratios can be used to effectively discriminate limestone and dolostone. This approach is completely independent of variations in illumination conditions of image pixels; therefore, topographic correction of the image is not needed. The approach performs better in areas where weathering of rock surfaces and vegetation cover are minimal.

Improved target detection for hyperspectral images using hybrid in-scene calibration

This paper presents a practical approach to target detection for hyperspectral images. In target detection, it is normally assumed that the ground truth target signatures collected in a laboratory are available and one then uses them to search for targets in a given image. However, directly applying the laboratory signatures to the real data is not appropriate due to environmental differences between the ground truth data and real data. Conventional atmospheric compensation schemes such as the use of MODTRAN can help to improve the target detection performance. However, the computational load is huge and thus real-time applications may prohibit this compensation approach. We present results of an alternative compensation technique known as in-scene compensation, which is appealing as no complicated techniques such as MODTRAN are needed. Two in-scene methods for visible near-infrared/short-wave infrared range have been developed in the literature: empirical line method (ELM) and vegetation normalization (VN). Both approaches have advantages and disadvantages. We propose a hybrid in-scene compensation method that can be considered as a combination of ELM and VN and we call our method ELM augmented VN (EAVN). One key advantage of EAVN is that it combines the advantages of ELM and VN and eliminates their disadvantages. Compared to ELM, there is no need for two or more known target pixels in the test scene. Compared to VN, there is no need for dark pixels. Extensive experimental results using ground-based sensor data showed that the EAVN algorithm provides excellent compensation to environmental changes. After compensation, the receiver operating characteristics performance of target detection has been significantly improved by orders of magnitude in a number of cases, as compared to two standard compensation methods: quick atmospheric correction and internal average relative reflectance correction.

Application of ASTER and Landsat 8 imagery data and mathematical evaluation method in detecting iron minerals contamination in the Chadormalu iron mine area, central Iran

Satellite images are widely used to map geological and environmental features at different map scales. The ability of visible to near-infrared (VNIR) scanner systems to map gossans, rich in iron and associated with weathered sulfide occurrences, as well as to characterize regoliths, is perhaps one of the most important current applications of this technology. Initial results of this study show that advanced space-borne thermal emission and reflection (ASTER), VNIR, and short-wave infrared radiometer scanner systems can be used successfully to map iron ores. By applying internal average relative reflectance, false color composite, minimum noise fraction transform, and mathematical evaluation method (MEM) techniques, iron contaminations were successfully detected in the Chadormalu iron mine area of central Iran. An attempt was also made to discriminate between the geogenic and anthropogenic iron contaminations in the vicinity of the Chadormalu iron deposit. This research compares ASTER and Landsat 8 data images and the MEM with the band ratio method in a full scope view scale and demonstrates ASTER image data capability in detecting iron contaminations in the Chadormalu area. This indicates that ASTER bands 3, 2, and 1 have a higher spatial (15 m) resolution compared with sensors used in previous works. In addition, the capability of the MEM in detecting Fe-contaminants, unlike the color judgments of the band ratio method, can discriminate between iron pollution in an alluvial plain and the Fe-contents of the host and country rocks in the study area. This study proved that Landsat 8 data illustrate exaggeration both in the MEM and band ratio final results (outputs) and cannot display iron contamination in detail.

Evaluation of relative atmospheric correction methods on ASTER VNIR–SWIR data in playa environment

Thermal emission and reflection radiometer (ASTER) L1B visible-near infrared and short wave-infrared (VNIR–SWIR) data were used to evaluate various relative calibration methods in playa environments. Internal average relative reflectance (IARR), log residual (LR), flat field (FF), and quick atmospheric correction (QUAC) methods were performed to convert at-sensor radiance data to apparent reflectance over several playas in Birjand, East of Iran. Results were evaluated comparing the calibrated image spectra of known locations and the laboratory spectra of field samples. The comparisons were done using spectral angle mapper (SAM) method and qualitative analysis of the spectral profiles. The SAM results and the qualitative analysis of the spectral profiles showed that spectra provided by applying FF method produced more similarities with laboratory spectra.

Quantitative and comparative examination of the spectral features characteristics of the surface reflectance information retrieved from the atmospherically corrected images of Hyperion

The retrieval of surface reflectance information from the same single pixel of the Hyperion image atmospherically corrected by using image-based [internal average relative reflectance (IARR), log residuals, and flat field] and radiative transfer model (RTM)-based [the fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) and the Atmospheric and Topographic Correction 2 (ATCOR-2)] approaches and the spectral feature characteristics of this information were quantitatively and comparatively examined based on measured ground spectral reflectance data. The spectral features quantitative analysis results of the reflectance data showed that spectral reflectances that are suitable and best fitting to the ground spectral reflectances which were obtained from the pixels of FLAASH, ATCOR-2, and flat field–corrected images, respectively. The retrieval of surface reflectance from the FLAASH-corrected image pixels, in general, produced high scores in spectral parameter analyses. Of the image-based approaches, only in flat field–derived reflectance data, results were obtained which are high and nearest to those of RTM and ground spectral reflectance data. Generally, low scores obtained in the spectral parameter analyses of the surface reflectance values retrieved from single pixels of IARR and log residuals-corrected images showed the results that fit worst to the measured ground spectral reflectance.

Evaluating Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for alteration zone enhancement in a semi‐arid area, northern Shahr‐e‐Babak, SE Iran

The visible–near infrared (VNIR) and short wave infrared (SWIR) spectral bands of both the level 1B, radiance at sensor, and level 2, AST_07 surface reflectance data products of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument were evaluated and compared for mapping the alteration zones around porphyry copper deposits and occurrences at the northern Shahr‐e‐Babak, SE Iran. The level 1B data were converted to reflectance using internal average relative reflectance (IARR) method whereas the AST_07 dataset was processed as delivered. The porphyry copper mineralization occurs in Eocene, andesitic and basaltic rocks with zonal alteration patterns that are concentric and almost symmetrically arranged. The spectral signatures of alteration index minerals collected from field samples and the United States Geological Survey (USGS) spectral reference library, were considered in directed principal component analysis (DPCA) and spectral angle mapping (SAM) algorithms. Carrying out the DPCA method on three spectral bands enhanced the alteration haloes in the last principal component (PC) images. Generating RGB colour composite images using these PC images differentiated three alteration zones from the host rocks. The SAM results of the IARR calibrated dataset discriminated the propylitic, argillic and phyllic alteration zones. It is concluded that the higher spectral resolution of ASTER instrument is effective for mineral mapping. However, the method of conversion from radiance to reflectance is critical to the validity of the outputs and that the pseudo‐reflectance method using the IARR process may be more reliable than the standard reflectance product.

The application of advanced space-borne thermal emission and reflection (ASTER) radiometer data in the detection of alteration in the Chadormalu paleocrater, Bafq region, Central Iran

Ore deposits are often produced by fluid flow processes that alter the mineralogy and chemistry of the country rock. One of the main objectives in developing a multi-spectral and hyperspectral sensor is to detect the optical characteristics of the Earth’s surface using hundreds of spectral bands. All previous studies show that remote sensing can recognize alteration to different degrees, using different spatial and spectral resolution sensors. The ASTER sensor measures reflected radiation in VNIR, SWIR and TIR electromagnetic energies. It is cheap and easily available. The alteration minerals in the Precambrian Chadormalu area (Chadormalu paleocrater and the related iron oxide deposit) have been investigated in the field and have been successfully detected by applying IARR (Internal Average Relative Reflectance), FCC (False Color Composite), Decorrelation-stretch, MNF (Minimum Noise Fraction Transform), correlated filter and MEM (Mathematical Evaluation Method) techniques on ASTER imageries. Sodic, potassic, and silicic–phyllic alteration patterns can be distinguished. This study shows that the capability and accuracy of the MEM method is better than 2 × 2 correlated filter techniques. Results indicate that pervasive hydrothermal ore-forming processes were important in the study area. Judging from the assemblage of alteration minerals, the Chadormalu iron deposit is an end-member of the Kiruna type – Iron oxide (Cu–Au) ore deposit continuum.

Determination of surface reflectance from raw hyperspectral data without simultaneous ground data measurements: A case study of the GER 63-channel sensor data acquired over Naan, Israel

Raw hyperspectral data that were acquired over Israel in 1989 by the GER 63-channel scanner were processed to provide surface reflectances seven years after the flight. Because no ground data measurements were available for the time of the flight, four atmospheric correction methods were applied: Atmospheric REMoval program (ATREM), Internal Average Relative Reflectance (IARR), Flat Field (FF) and Empirical Line (EL). Neither the ATREM program, which is an atmospheric model-based method, nor the IARR or the FF techniques, which are scene-dependent methods, were able to provide reasonable results. Whereas the failure of the ATREM program was probably because of a sensor's radiometric problem in the visible (VIS) region, the IARR and FF methods failed because of the relative complexity of the landscape. Of the three EL combinations examined, only one was able to convert the raw digital data into reasonable apparent reflectance information. Processing the data with this combination resulted in a good match between the spectra of selected targets taken from the image and their associated laboratory spectra. Samples, which were collected seven years after the actual flight, were used to assess the ability of each EL correction method to remove atmospheric attenuation. It was concluded that when working with such so-called 'hopeless' data, in order to obtain reasonable results, several combinations of the EL method need to be applied. The results of each method should then be judged, from both a spectral and a spatial perspective, against a separate set of samples, which were not a part of the correction procedure. In this case study, the spectral examination involved 20 samples, and the spatial examination involved two irrigated cotton plots. In the spectral examination, good agreement was obtained between the corrected spectra and the laboratory spectra. During the spatial examination it was possible to distinguish between two cotton plots having different soil water statuses by applying the Spectra Angle Mapper (SAM) classifier. It is felt that careful selection of samples is a prerequisite for achieving reasonable results using the EL correction technique. The samples should consist of albedo information representative of the study area, should have only minor changes related to the passage of time, and should be precisely identified on both the image and the ground. It was concluded that even a 'hopeless' raw data set, such as the current GER data, can be processed to yield reasonable physical information. Assuming that future hyperspectral data taken from orbit will not often be followed by simultaneous ground measurements, the results of this paper are promising.

Surface mineral mapping of Makhtesh Ramon Negev, Israel using GER 63 channel scanner data

Abstract Geophysical and Environmental Research (GER) 63 channel scanner data were used to map the surface mineralogy of Makhtesh Ramon, Negev, Israel. The data werecorrected to reflectance using the Internal Average Relative Reflectance (IARR) method and analysed using colour-composites, the Spectral Angle Mapper (SAM), and linear spectral unmixing (UNM). Results demonstrate that imaging spectrometer data can be used to produce detailed mineralogical maps in arid terrains even when no calibration measurements are made during the flights and no ground measurements are available. The mineralogical maps produced using the GER data compare favourably to the known surface mineralogy, however, they provide greater detail regarding composition and mineral abundance than the existing geological maps.

Comparison of three calibration techniques for utilization of GER 63-channel aircraft scanner data of Makhtesh Ramon, Negev, Israel : E. Ben-Dor, F. A. Kruse, A. B. Lefkoff & A. Banin, Photogrammetric Engineering & Remote Sensing, 60(11), 1994, pp 1339–1354

Geophysical Environmental Research (GER) 63-channel aircraft scanner data were used to map the mineralogy at Makhtesh Ramon in the Negev area of southern Israel. The data were calibrated to apparent reflectance using three techniques; the flat field (FF) method, the empirical line (EL) method, and the internal average relative reflectance (IARR) method. Results of these calibrations were evaluated by extracting image spectra and comparing them to library spectra for pure minerals and to laboratory spectra of samples collected from the field area. These comparisons show that each calibration method produces apparent reflectance spectra that contain useful mineralogical information. Care must be taken, however, to avoid over interpretation, as all of these calibrations may produce spectral artifacts

The Relationship Between the Size of Spatial Subsets of GER 63 Channel Scanner Data and the Quality of the Internal Average Relative Reflectance (IARR) Atmospheric Correction Technique

Abstract This study examined spatial subsets derived from one flightline of Geophysical Environmental Research (GER) 63 channel scanner data from Makhtesh Ramon, Israel, to determine the relationship between the size of spatial subsets and the quality of the Internal Average Relative Reflectance (IARR) atmospheric correction technique. The IARR procedure was run separately on spatial subsets containing 100, 53, 24, 9, 3, 0·16 and 0·02 per cent of the original data set. The correction quality was determined by comparing the reflectance spectra derived from each subset for a site containing the mineral kaolinite. Signal-to-noise ratio (SNR) calculations illustrate that the raw data quality does not vary significantly from one subset to another. The quality of the correction was affected only by changes in the ‘average reference spectrum’ (ARS) associated with the selected subsets. The short-wave infrared (SWIR) region (1·440−2·443 μm) was found to be less sensitive to ARS changes than the visible (VIS) regi...