Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada

This study investigated the potential value of integrating hyperspectral visible, near-infrared, and short-wave infrared imagery with multispectral thermal data for geological mapping. Two coregistered aerial data sets of Cuprite, Nevada were used: Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data, and MODIS/ASTER Airborne Simulator (MASTER) multispectral thermal data. Four classification methods were each applied to AVIRIS, MASTER, and a combined set. Confusion matrices were used to assess the classification accuracy. The assessment showed, in terms of kappa coefficient, that most classification methods applied to the combined data achieved a marked improvement compared to the results using either AVIRIS or MASTER thermal infrared (TIR) data alone. Spectral angle mapper (SAM) showed the best overall classification performance. Minimum distance classification had the second best accuracy, followed by spectral feature fitting (SFF) and maximum likelihood classification. The results of the study showed that SFF applied to the combination of AVIRIS with MASTER TIR data are especially valuable for identification of silicified alteration and quartzite, both of which exhibit distinctive features in the TIR region. SAM showed some advantages over SFF in dealing with multispectral TIR data, obtaining higher accuracy in discriminating low albedo volcanic rocks and limestone which do not have unique, distinguishing features in the TIR region.     

Lithological discrimination of the Phenaimata felsic–mafic complex,Gujarat, India,using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

This study deals with an evaluation of the efficacy of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image for lithological mapping. ASTER level-1B data in the visible near-infrared (VNIR), short wave infrared (SWIR) and thermal infrared (TIR) regions have been processed to generate a lithological map of the study area in and around the Phenaimata igneous complex, in mainland Gujarat, India. ASTER band combinations, band ratio images and spectral angle mapper (SAM) processing techniques were evaluated for mapping various lithologies. The reflectance and emissivity spectra of rock samples collected from the study area were obtained in the laboratory and were used as reference spectra for ASTER image analysis. The original data in the scaled digital number (DN) values were converted to radiance and then to relative reflectance by using a scene-derived correction technique prior to SAM classification. The SAM classification in the VNIR–SWIR region is found to be effective in differentiating felsic and mafic lithologies. The relative band depth (RBD) images were generated from the continuum-removed images of ASTER VNIR–SWIR bands. Four RBD combinations (3, 5, 6 and 8) were used to identify Al-OH (aluminium hydroxide), Fe-OH (iron hydroxide), Mg-OH (magnesium hydroxide) and CO₃ (carbonate) absorption from various lithological components. ASTER TIR spectral emittance data and the laboratory emissivity measurements show the presence of a number of discrete Si-O spectral features that can differentiate mafic and felsic rock types reflecting the lithological diversity around the regions of Phenaimata igneous complex. SAM classification using emittance data failed to distinguish the felsic and mafic lithology due to the wider spectral bandwidth. The felsic class comprises the granitoid composition of rocks. RBD12 and 13 images in the TIR region were used to derive the mafic index (MI) and the silica index (SI). The MI shows the highest value in regions of gabbro–basalt occurrence, while the SI indicates regions of high silica content. The MI is lowest in regions where granophyres occur. The complimentary attributes based on the spectral reflectance and emittance data resulted in the discrimination of silica-rich and silica-poor lithologies.     

Does single broadband or multispectral thermal data add information for classification of visible,near‐ and shortwave infrared imagery of urban areas?

The potential value of combining broadband and multispectral thermal infrared (TIR) data with multispectral and hyperspectral visible, near‐infrared (VNIR) and shortwave infrared (SWIR) data was investigated within the context of urban land‐cover classification. Using a case study of airborne Digital Airborne Imaging Spectrometer (DAIS) imagery of Strasbourg, France, the relative contribution of TIR wavelengths to classification accuracy was investigated for hyperspectral and simulated multispectral IKONOS, SPOT and Landsat Thematic Mapper (TM) bands. A support vector machines (SVM) classifier was used because this method was found to be very effective at handling the complex distributions of the heterogeneous land cover classes. The overall classification accuracy varied greatly with different band combinations. The inclusion of a single broad thermal band increased classification accuracy by as much as 20% for simulated IKONOS bands, but only 4% for hyperspectral VNIR and SWIR data. Adding multispectral TIR data raised the average accuracy approximately a further 10% for each band combination studied. Thermal wavelengths were found to be particularly useful for reducing the confusion between road and roof surfaces.     

Integrating visible,near‐infrared and short wave infrared hyperspectral and multispectral thermal imagery for geologic mapping: simulated data

Hyperspectral and thermal infrared (TIR) multispectral remote sensing have great potential for surface geological mapping. This paper investigates the potential impact of combining these data on the comparative accuracy of different classification methods. A series of simulated datasets based on the characteristics of Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER) sensors was created from surface reflectance and emissivity data derived from library spectra of 16 common minerals and rocks occurring in Cuprite, Nevada. System noise, illumination effects, the presence of vegetation, and spectral mixing were added to create the simulated data. Five commonly used classification algorithms, minimum distance, maximum likelihood classification, binary encoding, spectral angle mapper (SAM) and spectral feature fitting (SFF), were applied to all datasets. All the classification methods, excluding binary encoding, achieved nominal to significant improvement in overall accuracy when applied to the combined datasets in comparison to using only the AVIRIS dataset. Furthermore, certain classification methods of the combined datasets show a marked increase in individual rock or mineral class accuracies. Limestone, silicified and muscovite, for instance, show an improvement of almost 30% or greater in either producer's or user's accuracy using the combined datasets with SAM. SFF provides a great improvement in accuracy for limestone, quartz and muscovite. In terms of overall comparative accuracy for the individual and the combined datasets, maximum likelihood classification shows the best performance. For the simulated AVIRIS data, SFF was generally superior to SAM, although the accuracy of SAM applied to the combined datasets was slightly better than that of SFF. SAM applied to the combined datasets increases classification accuracy for some minerals and rocks which do not exhibit distinct absorption feature in the TIR region, while for SFF, only the accuracy of minerals and rocks with characteristic absorption features in the TIR region is improved.     

Spectral characterization and ASTER-based lithological mapping of an ophiolite complex: A case study from Neyriz ophiolite, SW Iran

The Neyriz ophiolite occurs along the Zagros suture zone in SW Iran, and is part of a 3000-km obduction belt thrusting over the edge of the Arabian continent during the late Cretaceous. This complex typically consists of altered dunites and peridotites, layered and massive gabbros, sheeted dykes and pillow lavas, and a thick sequence of radiolarites. Reflectance and emittance spectra of Neyriz ophiolite rock samples were measured in the laboratory and their spectra were used as endmembers in a spectral feature fitting (SFF) algorithm. Laboratory spectral reflectance measurements of field samples showed that in the visible through shortwave infrared (VNIR-SWIR) wavelength region the ultramafic and gabbroic rocks are characterized by ferrous-iron and Fe, MgOH spectral features, and the pillow lavas and radiolarites are characterized by spectral features of ferric-iron and AlOH. The laboratory spectral emittance spectra also revealed a wide wavelength range of SiO spectral features for the ophiolite rock units. After continuum removal of the spectra, the SFF classification method was applied to the VNIR + SWIR 9-band stack, and to the 11-band data set of SWIR and TIR data sets of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor, using field spectra as training sets for evaluating the potential of these data sets in discriminating ophiolite rock units. Output results were compared with the geological map of the area and field observations, and were assessed by the use of confusion matrices. The assessment showed, in terms of kappa coefficient, that the SFF classification method with continuum removal applied to the SWIR data achieved excellent results, which were distinctively better than those obtained using VNIR + SWIR data and TIR data alone.     

Regression-based synergy of optical, shortwave infrared and microwave remote sensing for monitoring the grain-size of intertidal sediments

A method is developed for monitoring the sediment grain-size of intertidal flats in the Westerschelde (southwest Netherlands), using information from both space-borne microwave (SAR) and optical/shortwave infrared remote sensing. Estimates of the backscattering coefficient were extracted from time-series of C-band ERS SAR imagery. Surface reflectance in the visible, near-infrared (VNIR) and shortwave infrared (SWIR) part of the electromagnetic spectrum, as well as spectral indices, were derived from matching multi-temporal Landsat TM imagery. In addition, surface reflectances were derived from a set of airborne multispectral (VNIR) CASI images, and hyperspectral (VNIR) measurements using a field spectroradiometer. The data were related to matching field measurements of surface characteristics, including sediment properties. Regression-based algorithms were developed to map the spatio-temporal distribution of mud content using (a) the C-band SAR backscattering coefficient, (b) surface reflectance in the green and SWIR, and (c) a combination of these, with corroborative field measurements. Mud content of the sediment has been successfully mapped by all three algorithms, but a combination of information from microwave and VNIR/SWIR provided best results. The algorithms were generally consistent in time, making them suitable for generating time-series and for monitoring. However, they should be validated and calibrated in order to be applicable to other intertidal areas.     

SEBASS hyperspectral thermal infrared data: surface emissivity measurement and mineral mapping

This study focuses on mapping surface minerals using a new hyperspectral thermal infrared (TIR) sensor: the spatially enhanced broadband array spectrograph system (SEBASS). SEBASS measures radiance in 128 contiguous spectral channels in the 7.5- to 13.5-μm region with a ground spatial resolution of 2 m. In September 1999, three SEBASS flight lines were acquired over Virginia City and Steamboat Springs, Nevada. At-sensor data were corrected for atmospheric effects using an empirical method that derives the atmospheric characteristics from the scene itself, rather than relying on a predicted model. The apparent surface radiance data were reduced to surface emissivity using an emissivity normalization technique to remove the effects of temperature. Mineral maps were created with a pixel classification routine based on matching instrument- and laboratory-measured emissivity spectra, similar to methods used for other hyperspectral data sets (e.g. AVIRIS). Linear mixtures of library spectra match SEBASS spectra reasonably well, and silicate and sulfate minerals mapped remotely, agree with the dominant minerals identified with laboratory X-ray powder diffraction and spectroscopic analyses of field samples. Though improvements in instrument calibration, atmospheric correction, and information extraction would improve the ability to map more pixels, these hyperspectral TIR data nevertheless show significant advancement over multispectral thermal imaging by mapping surface materials and lithologic units with subtle spectral differences in mineralogy.     

Spectroscopic characterization of oils yielded from Brazilian offshore basins: Potential applications of remote sensing

Spectroscopy is the basis to detect and characterize offshore hydrocarbon (HC) seeps through optical remote sensing. Diagnostic spectral features of HCs are linked to their chemical composition and fundamental molecular vibrations (SWIR-TIR features), as well as overtones and combinations of these vibrations (VNIR-SWIR). These features allow for the characterization of oil, oil on water and emulsified oil. This work shows the results of lab and field spectral measurements of 17 petroleum samples yielded from key, oil-rich sedimentary basins in Brazil. Measurements comprised reflectance data (VNIR- SWIR), Attenuated Total Reflectance (ATR), Directional Hemispherical Reflectance (DHR), and emissivity data (TIR). These spectra were analyzed by multivariate techniques, such as Principal Components Analysis (PCA) and Partial Least-Square analysis (PLS). The experimental results indicate that for the VNIR-SWIR range: (i) spectral features can be recognized for crude oil, emulsified oil and oil on ocean water; (ii) different oil types can be qualitatively distinguished based on these features (i.e. light or heavy), even considering oil on water; (iii) the same applies for oil measurements simulated at the spectral resolution of hyperspectral (357-bands/ProSpecTIR) and multispectral (9-bands/ASTER) sensors. Within TIR wavelengths (3-14 μm), typical HC spectral features can also be resolved and oil types qualitatively discriminated using PCA/PLS, including both full-resolution spectra and spectra resampled to hyperspectral sensor (128-bands/SEBASS). However, despite the fact that oil emissivity is always lower than that of water, such separation seems unfeasible using 8-12 μm TIR features only; emissivity spectra are essentially flat for all samples in this interval. This research demonstrated that oil can be qualitatively distinguished based on both VNIR-SWIR and TIR spectroscopy data, with important implications for remote off-shore oil exploration and classification of oil leakages.     

Lithologic mapping in the Oscar II Coast area,Graham Land,Antarctic Peninsula using ASTER data

The results of the first attempt to use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for the purposes of lithologic mapping on the Antarctic Peninsula are presented for an area on the Oscar II Coast, eastern Graham Land. This study included undertaking laboratory reflectance spectroscopy of ～70 rock samples from the study area and spectral lithologic analysis of two ASTER scenes. Spectra of the granitoids, silicic volcanic/volcaniclastic and terrestrial sedimentary rocks in the study area display a limited range of absorption features associated with muscovite, smectite and chlorite that are generally present as the alteration products of regional metamorphism. ASTER data analysis was undertaken using the reflective bands of the Level 1B registered radiance at-sensor data and the standard thermal infrared (TIR) emissivity product (AST05). For both wavelength regions, standard qualitative image processing methods were employed to define image end-members that were used as reference within Matched Filter (MF) processing procedures. The results were interpreted with reference to existing field observations, and photogeologic analysis of the ASTER visible to near-infrared (VNIR)/shortwave infrared (SWIR) data was used to resolve ambiguities in the spectral mapping results. The results have enabled the discrimination of most of the major lithologic groups within the study area as well as delineation of hydrothermal alteration zones of propylitic, and argillic grades associated with the Mesozoic Mapple Formation volcanics. The results have extended the mapped coverage of the Mapple Formation into un-investigated regions further north and validated previously inferred geological observations concerning other rocks throughout the study area. The outcomes will enable important revisions to be made to the existing geological map of the Oscar II Coast and demonstrate that ASTER data offers potential for improving geological mapping coverage across the Antarctic Peninsula.     

Non-photosynthetic vegetation biomass estimation in semiarid Canadian mixed grasslands using ground hyperspectral data,Landsat 8 OLI,and Sentinel-2 images

ABSTRACT

Research on quantifying non-photosynthetic vegetation (NPV) with optical remote-sensing approaches has been focusing on optically distinguishing NPV from green vegetation and bare soil. With a very similar spectral response curve to NPV, dry moss is a significant component in semiarid mixed grasslands and plays a large role in NPV estimation. However, limited attention has been paid to this role. We investigated the potential of optical remote sensing to distinguish NPV biomass in semiarid grasslands characterized by NPV, biological soil crust dominated by moss and lichen, and bare soil. First, hyperspectral spectral indices were examined to determine the most useful spectral wavelength regions for NPV biomass estimation. Second, multispectral red-edge indices and shortwave infrared (SWIR) indices were simulated based on Landsat 8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument band reflectance, respectively, to determine the most suitable multispectral indices for NPV estimation. The potential multispectral indices were then applied to Landsat 8 OLI images and Sentinel-2A images acquired in early, middle, peak, and early senescence growing seasons to investigate the potential of satellite images for quantifying NPV biomass. Our results indicated that hyperspectral red-edge indices, modified simple ratio, modified red-edge normalized difference vegetation index (mNDVI₇₀₅), and normalized difference vegetation index (NDVI₇₀₅) are better than the SWIR hyperspectral indices, including cellulose absorption index for quantifying NPV biomass. The simulated multispectral red-edge spectral indices (NDVI_red-edge and mNDVI_red-edge) demonstrate good and comparable performance on quantifying NPV biomass with SWIR multispectral indices (normalized difference index [NDI5 and NDI7] and soil-adjusted corn residue index). Nevertheless, the multispectral indices derived from Landsat 8 OLI and Sentinel-2 images have limited potential for NPV biomass estimation.     

Identifying plant species using mid-wave infrared (2.5–6 μm) and thermal infrared (8–14 μm) emissivity spectra

Plant species discrimination using remote sensing is generally limited by the similarity of their reflectance spectra in the visible, NIR and SWIR domains. Laboratory measured emissivity spectra in the mid infrared (MIR; 2.5 μm–6 μm) and the thermal infrared (TIR; 8 μm–14 μm) domain of different plant species, however, reveal significant differences. It is anticipated that with the advances in airborne and space borne hyperspectral thermal sensors, differentiation between plant species may improve. The laboratory emissivity spectra of thirteen common broad leaved species, comprising 3024 spectral bands in the MIR and TIR, were analyzed. For each wavelength the differences between the species were tested for significance using the one way analysis of variance (ANOVA) with the post-hoc Tukey HSD test. The emissivity spectra of the analyzed species were found to be statistically different at various wavebands. Subsequently, six spectral bands were selected (based on the histogram of separable pairs of species for each waveband) to quantify the separability between each species pair based on the Jefferies Matusita (JM) distance. Out of 78 combinations, 76 pairs had a significantly different JM distance. This means that careful selection of hyperspectral bands in the MIR and TIR (2.5 μm–14 μm) results in reliable species discrimination.     

Detection of sandy soil surfaces using ASTER‐derived reflectance,emissivity and elevation data: potential for the identification of land degradation

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) reflectance and emissivity data were used to discriminate nonphotosynthetic vegetation (NPV) from exposed soils, to produce a topsoil texture image, and to relate sand fraction estimates with elevation data in an agricultural area of central Brazil. The results show that the combination of the shortwave infrared (SWIR) bands 5 and 6 (hydroxyl absorption band) and thermal infrared (TIR) bands 10 and 14 (quartz reststrahlen feature) discriminated dark red clayey soils and bright sandy soils from NPV (crop litter), respectively. The ratio of the bands 10 and 14 was correlated with laboratory measured total sand fraction. When applied to the image and associated with topography, a predominance of sandy soil surfaces at lower elevations and clayey soil surfaces at higher elevations was observed. Areas presenting the largest sand fraction values, identified from ASTER band 10/14 emissivity ratio, were coincident with land degradation processes.     

Utilization of Hyperion data over Dongargarh,India, for mapping altered/weathered and clay minerals along with field spectral measurements

Hyperion data acquired over Dongargarh area, Chattisgarh (India), in December 2006 have been analysed to identify dominant mineral types present in the area, with special emphasis on mapping the altered/weathered and clay minerals present in the rocks and soils. Various advanced spectral processes such as reflectance calibration of the Hyperion data, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) have been used for comparison/mapping in conjunction with spectra of rocks and soils that have been collected in the field using Analytical Spectral Devices's FieldSpec instrument. In this study, 40 shortwave infrared channels ranging from 2.0 to 2.4 μm were analysed mainly to identify and map the major altered/weathered and clay minerals by studying the absorption bands around the 2.2 and 2.3 μm wavelength regions. The absorption characteristics were the results of O–H stretching in the lattices of various hydrous minerals, in particular, clay minerals, constituting altered/weathered rocks and soils. SAM and SFF techniques implemented in Spectral Analyst were applied to identify the minerals present in the scene. A score of 0–1 was generated for both SAM and SFF, where a value of 1 indicated a perfect match showing the exact mineral type. Endmember spectra were matched with those of the minerals as available in the United States Geological Survey Spectral Library. Four minerals, oligoclase, rectorite, kaolinite and desert varnish, have been identified in the studied area. The SAM classifier was then applied to produce a mineral map over a subset of the Hyperion scene. The dominant lithology of the area included Dongargarh granite, Bijli rhyolite and Pitepani volcanics of Palaeo-Proterozoic age. Feldspar is one of the most dominant mineral constituents of all the above-mentioned rocks, which is highly susceptible to chemical weathering and produces various types of clay minerals. Oligoclase (a feldspar) was found in these areas where mostly rock outcrops were encountered. Kaolinite was also found mainly near exposed rocks, as it was formed due to the weathering of feldspar. Rectorite is the other clay mineral type that is observed mostly in the southern part of the studied area, where Bijli rhyolite dominates the lithology. However, the most predominant mineral type coating observed in this study is desert varnish, which is nothing but an assemblage of very fine clay minerals and forms a thin veneer on rock/soil surfaces, rendering a dark appearance to the latter. Thus, from this study, it could be inferred that Hyperion data can be well utilized to identify and map altered/weathered and clay minerals based on the study of the shape, size and position of spectral absorption features, which were otherwise absent in the signatures of the broadband sensors.     

ASTER spectral analysis of ultramafic lamprophyres (carbonatites and aillikites) within the Batain Nappe,northeastern margin of Oman: a proposal developed for spectral absorption

We developed a scientific proposal on spectral absorption in remote sensing and a new image-processing method that is purely based on multispectral satellite image spectra to map ultramafic lamprophyre and carbonatite occurrences. The proposed method provides a simple, yet efficient, tool that will help exploration geologists. In this proposal, in which the spectral absorption is applicable to all satellite images obtained in visible, reflected infrared, and thermal infrared spectral wavelength regions, we found that the carbonatites appear white in colour on a greyscale or RGB thermal infrared image obtained in the thermal infrared wavelength region (3–15 μm) due to molecular emission of thermal energy by such carbonate content, particularly the wavelength recorded by the sensor and that the variation of absorption in spectral bands of an outcrop is due to the differences in percentage of carbonate content or the spectral, spatial, radiometric, or temporal resolution of satellite data or the occurrences of carbonatites to incident energy. The results were confirmed by studying the spectral absorption characteristics of carbonatites in selected world occurrences including parts of Batain Nappe, Oman; Fuerteventura (Canary Islands), Spain; Mount Homa, Kenya; Ol Doinyo Lengai, Tanzania; Mount Weld region, (Laverton), Australia, and Phalaborwa region, South Africa, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Thematic Mapper (TM) satellite data. A subsequent study of visible near-infrared (VNIR) and shortwave infrared (SWIR) ASTER spectral bands of Early Cretaceous alkaline ultramafic rocks of Batain Nappe, along the northeastern margin of Oman to map for the occurrences of carbonatite and aillikite (ultramafic lamprophyres) dikes and plugs, showed their detection mainly by the diagnostic CO₃ absorption (2.31–2.33 μm) in ASTER SWIR band 8. The results of image interpretations were verified and confirmed in the field and were validated through the study of laboratory analyses. A few more carbonatite dike occurrences were interpreted directly over the greyscale image of ASTER bands and true-colour interpretations of a Google Earth image along this margin. The carbonatites and aillikite occurrences of the area are rich in apatite, iron oxide, phlogopite, and REE-rich minerals and warrant new exploration projects.     

Temperature and emissivity separation from multispectral thermal infrared observations

Knowledge of the surface emissivity is important for determining the radiation balance at the land surface. For heavily vegetated surfaces, there is little problem since the emissivity is relatively uniform and close to one. For arid lands with sparse vegetation, the problem is more difficult because the emissivity of the exposed soils and rocks is highly variable. With multispectral thermal infrared (TIR) observations, it is possible to estimate the spectral emissivity variation for these surfaces. We present data from the TIMS (Thermal Infrared Multispectral Scanner) instrument, which has six channels in the 8- to 12-μm region. TIMS is a prototype of the TIR portion of the ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) instrument on NASA's Terra (EOS-AM1) platform launched in December 1999. The Temperature Emissivity Separation (TES) algorithm, developed for use with ASTER data, is used to extract the temperature and six emissivities from the six channels of TIMS data. The algorithm makes use of the empirical relation between the range of observed emissivities and their minimum value. This approach was applied to the TIMS data acquired over the USDA/ARS Jornada Experimental Range in New Mexico. The Jornada site is typical of a desert grassland where the main vegetation components are grass (black grama) and shrubs (primarily mesquite) in the degraded grassland. The data presented here are from flights at a range of altitudes from 800 to 5000 m, yielding a pixel resolution from 3 to 12 m. The resulting spectral emissivities are in qualitative agreement with laboratory measurements of the emissivity for the quartz rich soils of the site. The derived surface temperatures agree with ground measurements within the standard deviations of both sets of observations. The results for the 10.8- and 11.7-μm channels show limited variation of the emissivity values over the mesquite and grass sites indicating that split window approaches may be possible for conditions like these.     

谐波分析光谱角制图高光谱影像分类

目的 针对光谱角制图(SAM)分类算法对高光谱像元光谱曲线的局部特征和其辐射强度不敏感,而且易受噪声和维数灾难影响,致使分类效率低和精度较差等缺陷,将谐波分析(HA)技术引入到SAM高光谱影像分类中,提出一种基于谐波分析的光谱角制图(HA-SAM)高光谱影像分类算法.方法 利用HA技术将高光谱影像从光谱维变换到能量谱特征维空间,并提取低次谐波分量及特征系数(谐波余项、相位和振幅),用特征系数组成的向量代替光谱向量,对高光谱影像进行SAM分类.结果 将SAM和HA-SAM同时应用于EO-1卫星的Hyperion高光谱影像分类,通过对比和分析,验证了HA-SAM的优越性,再选择AVIRIS(airborne visible infrared imaging spectrometer)高光谱影像对HA-SAM进行验证,结果表明该算法具有较强的普适性.结论 HA-SAM提高了传统SAM高光谱影像分类的效率和精度,而且适用性较强具有良好的应用前景.     

Sediment facies classification of a sandy shoreline by means of airborne imaging spectroscopy

Airborne imaging spectroscopy data (AISA Eagle and HyMap) were applied to classify the sediments of a sandy beach in seven sand type classes. On the AISA‐Eagle data, several classification strategies were tried out and compared with each other. The best classification results were obtained applying a linear discriminant classifier (LDC) in combination with feature selection based on sequential floating forward search (SFFS). The statistical LDC was used in a multiple binary approach. In the first step, the original bands were used in the classification, but transformation of the bands to wavelet coefficients enhanced the accuracy obtained. The combination of LDC with SFFS resulted in an overall accuracy of 82% (using three wavelet coefficients). Replacing the LDC with the non‐statistical SAM algorithm reduced the overall accuracy to 74% (using all bands or wavelet coefficients). When applying LDC, the optimal number of bands/wavelet coefficients to be used was defined: using more than two bands or three wavelet coefficients did not result in a higher classification accuracy. Finally, the HyMap data, featuring 126 bands in the VNIR‐SWIR range, were used to demonstrate that the VNIR range outperforms the SWIR range for this application.     

Optimal field sampling for targeting minerals using hyperspectral data

This paper presents a statistical method for deriving optimal spatial sampling schemes. It focuses on ground verification of minerals derived from hyperspectral data. Spectral angle mapper (SAM) and spectral feature fitting (SFF) classification techniques were applied to obtain rule mineral images. Each pixel in these rule images represents the similarity between the corresponding pixel in the hyperspectral image to a reference spectrum. The rule images provide weights that are utilized in objective functions of the sampling schemes which are optimized through a process of simulated annealing. A HyMAP 126-channel airborne hyperspectral data acquired in 2003 over the Rodalquilar area in Spain serves as an application to target those pixels with the highest likelihood of occurrence of a specific mineral and as a collection the location of these sampling points selected represent the distribution of that particular mineral. In this area, alunite being a predominant mineral in the alteration zones was chosen as the target mineral. Three weight functions are defined to intensively sample areas where a high probability and abundance of alunite occurs. Weight function I uses binary weights derived from the SAM classification image, leading to an even distribution of sampling points over the region of interest. Weight function II uses scaled weights derived from the SAM rule image. Sample points are arranged more intensely in areas of abundance of alunite. Weight function III combines information from several different rule image classifications. Sampling points are distributed more intensely in regions of high probable alunite as classified by both SAM and SFF, thus representing the purest of pixels. This method leads to an efficient distribution of sample points, on the basis of a user-defined objective.     

Spectral unmixing of vegetation,soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations

Remote measurements of the fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil are critical to understanding climate and land-use controls over the functional properties of arid and semi-arid ecosystems. Spectral mixture analysis is a method employed to estimate PV, NPV and bare soil extent from multispectral and hyperspectral imagery. To date, no studies have systematically compared multispectral and hyperspectral sampling schemes for quantifying PV, NPV and bare soil covers using spectral mixture models. We tested the accuracy and precision of spectral mixture analysis in arid shrubland and grassland sites of the Chihuahuan Desert, New Mexico, USA using the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). A general, probabilistic spectral mixture model, Auto-MCU, was developed that allows for automated sub-pixel cover analysis using any number or combination of optical wavelength samples. The model was tested with five different hyperspectral sampling schemes available from the AVIRIS data as well as with data convolved to Landsat TM, Terra MODIS, and Terra ASTER optical channels. Full-range (0.4-2.5 w m) sampling strategies using the most common hyperspectral or multispectral channels consistently over-estimated bare soil extent and under-estimated PV cover in our shrubland and grassland sites. This was due to bright soil reflectance relative to PV reflectance in visible, near-IR, and shortwave-IR channels. However, by utilizing the shortwave-IR2 region (SWIR2; 2.0-2.3 w m) with a procedure that normalizes all reflectance values to 2.03 w m, the sub-pixel fractional covers of PV, NPV and bare soil constituents were accurately estimated. AVIRIS is one of the few sensors that can provide the spectral coverage and signal-to-noise ratio in the SWIR2 to carry out this particular analysis. ASTER, with its 5-channel SWIR2 sampling, provides some means for isolating bare soil fractional cover within image pixels, but additional studies are needed to verify the results.     

EO-1 Hyperion数据的预处理、特征提取和岩性填图研究

EO-1 Hyperion传感器是第一个可以获取可见光与近红外以及短波红外波长范围光谱信息的星载高光谱传感器。本文以美国最早的金矿采矿区之一,加利福尼亚州东南巧克力山的Rainbow金矿区作为研究案例,探讨了Hyperion数据的预处理方法,专题信息提取与填图,评估了Hyperion高光谱数据在识别与金矿有关的岩性类型的应用价值。结果表明,本文所提出的Hyperion数据预处理方法是有效的,MNF方法能有效用于Hyperion数据维数的降低和数据冗余的去除以及分类特征的提取。最大似然分类器能够有效地从Hyperion高光谱数据中提取与金矿相关的重要岩体信息,所得到的岩性单元与地质图上对应的岩性分布具有很好的一致性。岩体分类的总精度为86%。该研究表明,Hyperion高光谱数据能够很好识别有细微光谱差别的岩性,因而在地质学研究与找矿领域有着良好的应用前景。