Mapping alteration minerals at prospect,outcrop and drill core scales using imaging spectrometry

Imaging spectrometer data (also known as ‘hyperspectral imagery’ or HSI) are well established for detailed mineral mapping from airborne and satellite systems. Overhead data, however, have substantial additional potential when used together with ground-based measurements. An imaging spectrometer system was used to acquire airborne measurements and to image in-place outcrops (mine walls) and boxed drill core and rock chips using modified sensor-mounting configurations. Data were acquired at 5 nm nominal spectral resolution in 360 channels from 0.4 to 2.45 μm. Analysis results using standardized hyperspectral methodologies demonstrate rapid extraction of representative mineral spectra and mapping of mineral distributions and abundances in map-plan, with core depth, and on the mine walls. The examples shown highlight the capabilities of these data for mineral mapping. Integration of these approaches promotes improved understanding of relations between geology, alteration and spectral signatures in three dimensions and should lead to improved efficiency of mine development, operations and ultimately effective mine closure.     

A comparative study of signal transformation techniques in automated spectral unmixing of infrared spectra for remote sensing applications

From geological and planetary exploration perspectives, automated sub-pixel classification of hyperspectral data is the most difficult task as it involves blind unmixing with library spectra of minerals. In this study, we demonstrate a procedure involving spectral transformation and linear unmixing to achieve the above task. For this purpose, infrared spectra of rocks from the spectral library, field, and remotely sensed hyperspectral image cube were used. Potential spectra of minerals for unmixing rock spectra were drawn from the library based on similarity of absorption features measured using Pearson correlation coefficient. Eight transformation techniques namely, first derivative, fast Fourier transform, discrete wavelet transform, Hilbert–Huang transform, crude low pass filter, S-transform, binary encoding, spectral effective peak matching, and two sparsity-based techniques (orthogonal matching pursuit, sparse unmixing via variable splitting, and augmented Lagrangian) were evaluated. Subsequently, minerals identified by above techniques were unmixed by linear mixture model (LMM) to decipher mineralogical composition and abundance. Results of LMM achieved using fully constrained least-square-estimation-based quadratic programming optimization approach were evaluated by conventional procedures such as X-ray diffraction and microscopy. In the case of image cube, endmembers derived using minimum noise fraction and pixel purity index were subjected to above procedure. It is evident that the discrete-wavelet-transformation-based approach produced excellent and meaningful results due to its flexibility in scaling the data and capability to handle noisy spectra. It is interesting to note that the adopted procedure could perform sub-pixel classification of image cube automatically and identify predominance of dolomite in limestone and sodium in alunite based on subtle differences in absorption positions.     

HyMap hyperspectral remote sensing to detect hydrocarbons

The ability of airborne hyperspectral remote sensing methods to detect hydrocarbons was investigated by the Federal Institute of Geosciences and Natural Resources. Reference areas of defined geometry and chemical properties were prepared, e.g. sandy soil, oil-contaminated soil, grass, plastic tarpaulins. The aim of the study was to collect hyperspectral data from these areas and simultaneously determine their spectra with the infrared intelligent spectroradiometer GER Mark V IRIS. The data corrections and further processing were based on data provided by a field spectrometer. This study showed that airborne hyperspectral remote sensing can be used to detect hydrocarbons efficiently. Hydrocarbon-bearing substances are characterized by typical absorption features in the spectra. The availability of the high signal-to-noise-ratio HyMap hyperspectral imaging system permits these features to be recognized in the pixel spectra even if they are not very pronounced. Oil contaminated soil and other materials containing hydrocarbons can be detected and located directly and unambiguously by image processing focused on the spectral characteristics of hydrocarbons. By this procedure, atmospheric correction of the HyMap data is not necessary.     

近红外高光谱图像数据预测技术

目的 受到传感器光谱响应范围的影响,可见光区域和近红外区域（400~2 500 nm）的高光谱数据通常使用不同的感光芯片进行成像,现有这一光谱区域典型的高光谱成像系统,如AVIRIS （airborne visible infrared imaging spectrometer）成像光谱仪,通常由多组感光芯片组成,整个成像系统成本和体积通常比较大,严重限制了该谱段高光谱探测技术的发展。为了能够扩展单感光芯片成像系统获得的高光谱图像的光谱范围,本文探索基于卷积神经网络的近红外光谱数据预测技术。方法 结合AVIRIS成像光谱仪的光谱配置,设计了基于残差学习的红外谱段图像预测网络,利用计算成像的方式从可见光范围的高光谱图像预测出近红外波段的光谱图像,并在典型的卫星高光谱遥感数据上进行红外光谱预测重构和基于重构的数据分类实验,以验证论文提出的红外光谱数据预测技术的可行性以及有效性。结果 本文设计的预测网络在Cuprite数据集上得到的预测近红外图像峰值信噪比为40.145 dB,结构相似度为0.996,光谱角为0.777 rad;在Salinas数据集上得到的预测近红外图像峰值信噪比为39.55 dB,结构相似性为0.997,光谱角为1.78 rad。在分类实验中,相比于只使用可见光图像,利用预测的近红外图像使得支持向量机（support vector machine,SVM）的准确率提升了0.6%,LeNet的准确率提升了1.1%。结论 基于AVIRIS传感器获取的两组典型卫星高光谱数据实验表明,本文提出的红外光谱数据预测技术不仅可基于计算成像的方式扩展可见光光谱成像系统的光谱成像范围,对于减小成像系统体积和质量具有重要意义,而且可有效提高可见光区域光谱图像数据在典型应用中的处理性能,对于提高高光谱数据处理精度提供新的技术支撑。     

Mapping the degree of serpentinization within ultramafic rock bodies using imaging spectrometer data

In 0·4-2·5 μm reflectance spectra of serpentinized peridotites and synthetic olivine-serpentine-magnetite mineral mixtures, serpentinization is responsible for a decrease in contrast of olivine-pyroxene iron absorption features and an appearance and increase in OH^? absorption features near 1·4 μm and 2·3 μm. It is demonstrated that the degree of serpentinization is correlated positively with the depth of the 2·3 μm absorption feature, although small amounts of magnetite may obscure the spectral contrast and decrease the overall brightness of weakly serpentinized samples. This linear relationship is applied to map the degree of serpentinization from GER 63-channel imaging spectrometer data using the following methodology: (1) vegetation masking, (2) calculating the absorption band-depth of the 2·3 μm absorption feature, (3) translating this value into percentage serpentine-group minerals using an empirical linear model, and (4) estimating the degree of serpentinization at the remaining locations using conditional simulation techniques. Comparison or the results of the simulation with 49 field samples showed differences between + 33 per cent and ? 23 per cent serpentine-group minerals estimated.     

On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images

In this work, neural network-based models involved in hyperspectral image spectra separation are considered. Focus is on how to select the most highly informative samples for effectively training the neural architecture. This issue is addressed here by several new algorithms for intelligent selection of training samples: (1) a border-training algorithm (BTA) which selects training samples located in the vicinity of the hyperplanes that can optimally separate the classes; (2) a mixed-signature algorithm (MSA) which selects the most spectrally mixed pixels in the hyperspectral data as training samples; and (3) a morphological-erosion algorithm (MEA) which incorporates spatial information (via mathematical morphology concepts) to select spectrally mixed training samples located in spatially homogeneous regions. These algorithms, along with other standard techniques based on orthogonal projections and a simple Maximin-distance algorithm, are used to train a multi-layer perceptron (MLP), selected in this work as a representative neural architecture for spectral mixture analysis. Experimental results are provided using both a database of nonlinear mixed spectra with absolute ground truth and a set of real hyperspectral images, collected at different altitudes by the digital airborne imaging spectrometer (DAIS 7915) and reflective optics system imaging spectrometer (ROSIS) operating simultaneously at multiple spatial resolutions.     

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.     

Multi-spectral facies in prevalent carbonate strata of an area of Migiurtinia (northern Somalia): analysis and interpretation

Rock expression in a multi-spectral image is referred to as multi-spectral image facies. Thematic Mapper (TM) multi-spectral facies of sedimentary rock bodies outcropping in an overall arid area of northern Somalia were interpreted by: (1) comparison with laboratory reflectance spectra of weathered surfaces of rock samples from the study area; (2) considering the correlation between spectra of weathered and unweathered surfaces; and (3) petrographic analysis. Aerial photo analysis provided stratigraphic and morphological information.

The rocks studied comprise mainly carbonate and evaporitic strata. TM facies appeared to be related to rock composition, presence and nature of weathering processes, scale of the lithological variability, stratal pattern in the erosional profile and outcrop morphology. Specific weathering products are, in their turn, apparently related to properties of the host rocks, such as composition and texture. Six prominent 741 (red, green, blue) facies were identified: a red facies, diagnostic of lichen coating, masking carbonate absorptions in TM7; a yellow-green laterally persistent couplet, unchecked in the field; a banded brown and yellow facies, expression of carbonate shelf strata; a purplish-red facies indicating a silcritic duricrust; a yellow facies related to fine crystalline dolostones; a cyan facies due to strong absorption in band 7, diagnostic of hydrous sulphate minerals. The comparison with a classification of the rocks based on reflectance characteristics of unweathered samples facilitated the interpretation of the remote responses and the correlation with lithostratigraphic units previously recognized in the area by different authors.     

高光谱遥感岩矿识别填图的技术流程与主要技术方法综述

自20世纪80年代以来,伴随着多种航空和航天成像光谱仪的研制成功和投入运行,一系列的图像光谱基本处理手段和实验室岩矿光谱分析技术方法,如最小噪声分量(MNF)变换-像元纯度指数(Pixel Purity Index,PPI)-N维可视化(N-Dimensional Visualization)、岩石类型系统光谱分类、多变量统计分析、矿物光谱特征确认的概率分析、MGM技术等相继发展起来,从而为形成成像光谱岩矿填图的完整技术流程打下了坚实的技术基础。分析了多种岩矿光谱分析的技术方法,同时指出,不同方法具有各自的优、缺点,针对不同应用目标需要不同的矿物识别与岩矿填图方案;混合方法的开发应用和从可见光到微波波段的融合应用在未来更为重要。     

Ground-based hyperspectral imaging as a tool to identify different carbonate phases in natural cliffs

Recent research has shown hyperspectral imaging to be a powerful tool to distinguish carbonate phases with slight compositional differences on quarry cliff faces. The traditional remote sensing set-up uses an optimal short distance between the hyperspectral camera mounted on a tripod and a quarry wall characterized by a planar, mostly unweathered surface. Here we present results of a modified workflow geared to the application of ground-based hyperspectral imaging of rough and weathered cliff faces in order to map large scale dolomite bodies from a distance of up to several kilometres. The goal of the study was to determine unique spectral properties of fracture-controlled dolomite bodies in order to be able to distinguish them from a dolomitic host rock. In addition, the impact of weathering on carbonate phases and thus, the modification of the spectral signature between altered and unaltered carbonates is assessed. The spectral analysis is complemented by ICP-AES (inductively coupled plasma atomic emission spectroscopy) measurements of the spectrally measured powders. Furthermore, we examined the detection limits and characterisation potential of dolomite bodies from hyperspectral images captured at varying distances from cliff faces in the study area. Hyperspectral images of 10 natural cliffs distributed across the Central Oman Mountains were obtained with a Push broom scanner system. The high resolution of 5.45 nm (288 bands in total) enabled the visualization of small-scale changes in the near infrared continuous spectrum of all present lithofacies types. The determination of dolomite bodies of varying sizes (metre to hundreds of metres) on natural cliffs was achieved with the hyperspectral mapping approach and mapping results have been tested with the position of visually defined dolomite bodies on field panoramas. Spectra of natural cliffs contain a strong absorption peak indicative for iron which is absent in spectra of unweathered sample powders. However, ICP-AES analysis of powders revealed relatively low contents of iron of 12,392 ppm. The strong peaks in field images are interpreted as linked to intensive weathering associated with the precepitation of goethite, hematite, specularite and manganese as well as intensive dedolomitization. Dedolomitization is indicated by calcitic spectra derived from the dolomite bodies. The spectral difference of laboratory and field spectra interferes significantly the application of laboratory spectra of powdered samples for the identification of dolomite bodies in the field. Furthermore, the process of late dedolomitization puts an additional challenge on the determination of dolomite bodies. Due to these strong spectral variations between laboratory and field spectra, we recommend that the mapping approaches should not solely rely on spectral algorithms but also consider normal light field panoramas and representative outcrop analysis. We also note that the quality of resolution is too low for the determination of small-scale variations of diagenetic phases at distances larger than 4 km. However, when the limitations mentioned are taken into account, hyperspectral imaging proves to be a powerful tool that helps in the determination of the distribution of diagenetic phases, even in challenging conditions.     

Using hyperspectral data to quantify water-quality parameters in the Wabash River and its tributaries,Indiana

A hand-held spectrometer was used to collect above-water spectral measurements for measuring optically active water-quality characteristics of the Wabash River and its tributaries in Indiana. Water sampling was undertaken concurrent with spectral measurements to estimate concentrations of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and Sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using the corrected field spectra and in situ chl and TSS data. A subset of the field measurements was used for model development and the rest for model validation. Spectral characteristics indicative of waters dominated by different inherent optical properties (IOPs) were identified and used as the basis of selecting bands for empirical model development. It was found that the ratio of the reflectance peak at the red edge (704 nm) with the local minimum caused by chl absorption at 677 nm was a strong predictor of chl concentrations (coefficient of determination (R²) = 0.95). The reflectance peak at 704 nm was also a good predictor for TSS estimation (R² = 0.75). In addition, we also found that reflectance within the near-infrared (NIR) wavelengths (700–890 nm) all showed a strong correlation (0.85–0.91) with TSS concentrations and generated robust models. Results suggest that hyperspectral information provided by field spectrometer can be used to distinguish and quantify water-quality parameters under complex IOP conditions.     

基于EXCEL的高光谱影像光谱响应分析

高光谱遥感侧重于从光谱维角度对影像信息进行分析与处理。由于目前高光谱数据的处理技术跟不上数据获取技术,而已有的成熟的多光谱影像处理技术并不适合于处理高光谱数据,因此利用EXCEL软件展开了高光谱影像的地物光谱重建、光谱特征及其相关性分析、光谱微分计算、光谱向量相似性度量和信息提取等研究,并基于PHI（Pushbroom Hyperspectral Imager）航空高光谱影像像元光谱维矢量进行了光谱响应分析,实现信息监测和识别。     

Mapping mineralogical alteration using principal‐component analysis and matched filter processing in the Takab area,north‐west Iran,from ASTER data

The Takab area, located in north‐west Iran, is an important gold mineralized region with a long history of gold mining. The gold is associated with toxic metals/metalloids. In this study, Advanced Space Borne Thermal Emission and Reflection Radiometer data are evaluated for mapping gold and base‐metal mineralization through alteration mapping. Two different methods are used for argillic and silicic alteration mapping: selective principal‐component analysis and matched filter processing (MF). Running a selective principal‐component analysis using the main spectral characteristics of key alteration minerals enhanced the altered areas in PC2. MF using spectral library and laboratory spectra of the study area samples gave similar results. However, MF, using the image reference spectra from principal component (PC) images, produced the best results and indicated the advantage of using image spectra rather than library spectra in spectral mapping techniques. It seems that argillic alteration is more effective than silicic alteration for exploration purposes. It is suggested that alteration mapping can also be used to delineate areas contaminated by potentially toxic metals.     

Exploring spectral discrimination of grass species in African rangelands

The spectral reflectance from eight species of rangeland grasses in the Masai Mara Nature Reserve, Kenya, was measured using a laboratory-based spectrometer. There were statistically significant differences in the spectral reflectance between species-a result which is encouraging for future work on identifying, classifying, mapping and monitoring rangeland ecosystems from hyperspectral imagery. To date, hyperspectral imagery has been available only through airborne scanners, but the European Space Agency and the United States' National Aeronautics and Space Administration (NASA) both plan satellite missions. The second part of this paper describes the acquisition and analysis of hyperspectral data (CASI) coincident with ground plots. In these plots, the mix of grass species varied from pure (monospecific) patches through to mixes of four to five different species. Evidence is presented indicating that some species may be identified on the image, based on the laboratory-obtained spectra.     

Parallel processing of remotely sensed hyperspectral imagery: full‐pixel versus mixed‐pixel classification

The rapid development of space and computer technologies allows for the possibility to store huge amounts of remotely sensed image data, collected using airborne and satellite instruments. In particular, NASA is continuously gathering high‐dimensional image data with Earth observing hyperspectral sensors such as the Jet Propulsion Laboratory's airborne visible–infrared imaging spectrometer (AVIRIS), which measures reflected radiation in hundreds of narrow spectral bands at different wavelength channels for the same area on the surface of the Earth. The development of fast techniques for transforming massive amounts of hyperspectral data into scientific understanding is critical for space‐based Earth science and planetary exploration. Despite the growing interest in hyperspectral imaging research, only a few efforts have been devoted to the design of parallel implementations in the literature, and detailed comparisons of standardized parallel hyperspectral algorithms are currently unavailable. This paper compares several existing and new parallel processing techniques for pure and mixed‐pixel classification in hyperspectral imagery. The distinction of pure versus mixed‐pixel analysis is linked to the considered application domain, and results from the very rich spectral information available from hyperspectral instruments. In some cases, such information allows image analysts to overcome the constraints imposed by limited spatial resolution. In most cases, however, the spectral bands collected by hyperspectral instruments have high statistical correlation, and efficient parallel techniques are required to reduce the dimensionality of the data while retaining the spectral information that allows for the separation of the classes. In order to address this issue, this paper also develops a new parallel feature extraction algorithm that integrates the spatial and spectral information. The proposed technique is evaluated (from the viewpoint of both classification accuracy and parallel performance) and compared with other parallel techniques for dimensionality reduction and classification in the context of three representative application case studies: urban characterization, land‐cover classification in agriculture, and mapping of geological features, using AVIRIS data sets with detailed ground‐truth. Parallel performance is assessed using Thunderhead, a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. The detailed cross‐validation of parallel algorithms conducted in this work may specifically help image analysts in selection of parallel algorithms for specific applications. Copyright © 2008 John Wiley & Sons, Ltd.     

Locally adaptive linear mixture model-based super-resolution land-cover mapping based on a structure tensor

Super-resolution land-cover mapping (SRM) is a technique for generating land-cover thematic maps with a finer spatial resolution than the input image. Linear mixture model-based SRM (LSRM) is applied directly to a remotely sensed image and is composed of a spatial term that integrates the land-cover spatial pattern prior information, a spectral term that assumes that the spectral signature of each mixed pixel is composed of a weighted linear sum of endmember spectral signatures within that pixel and a balance parameter that defines the weight of the spatial term. The traditional LSRM adopts an isotropic spatial autocorrelation model in the land-cover spatial term for different classes and a fixed balance parameter for the entire image, and ignores the image local properties. The class boundaries are at risk of oversmoothing and may be imprecise, and the homogeneous regions may be unsmoothed and contain speckle-like artefacts in the result. This study proposes a locally adaptive LSRM (LA-LSRM) that integrates image local properties to predict fine spatial resolution pixel labels. The structure tensor is applied to detect the image local information. The LA-LSRM spatial term is locally adaptive and is composed of an anisotropic spatial autocorrelation model in which the spatial autocorrelation orientations of different classes may vary. The LA-LSRM balance parameter is locally adaptive to the different regions of the image. Such parameter obtains a relatively large value when the fine-resolution pixel is located in the homogeneous region to remove speckle-like artefacts and a relatively small value when the fine-resolution pixel is at the class boundary to preserve the edge. The LA-LSRM performance was assessed using a simulated multi-spectral image, an IKONOS multi-spectral image, a hyperspectral image produced by Airborne Visible/Infrared Imaging Spectrometer and a hyperspectral image produced by reflective optics system imaging spectrometer. Results show that the homogeneous regions were smoothed, the boundaries were better preserved and the overall accuracies were increased by LA-LSRM compared with traditional LSRM in all experiments.     

Monitoring salt crusts on an AMD contaminated coastal wetland using hyperspectral Hyperion data (Estuary of the River Odiel,SW Spain)

The aim of this work is to establish preliminary spectral trends focused on the development of salt crusts in the marsh located at the mouth of the River Odiel (SW Spain) based on maps from archive Hyperion data. Temporal monitoring of salt efflorescence on the marshes at the mouth of the contaminated river is carried out using hyperspectral space imagery. Climate variability relationships are made based on well-known spectral features related to vegetation and shallow water, using both archive spectral libraries and field local spectra. The observations point to spectral and geomorphological indicators related to salt crust development that can be monitored through image processing supported by field and laboratory spectral data, on a repeatable basis. Future mapping of a larger sequence of images under different climate regimes and wider tidal ranges would improve the estimation of spectral features and thus ensure routine monitoring of salt crusts with hyperspectral data. The study of acid and salt environments, which limit biota development, could be improved by monitoring that employs hyperspectral remote sensing as a useful tool.     

Hyperspectral aerial images. A valuable tool for submerged vegetation recognition in the Orbetello Lagoons,Italy

Results obtained in mapping algal belts in the Orbetello Lagoons are described using Daedalus/MIVIS hyperspectral scanner aerial images. MIVIS has a spectral coverage in the Visible, Near-IR, Mid-IR and Thermal-IR regions, with 102 channels. The objective of the work is a procedure for the algal species recognition, using methods of spectral data analysis. The 1-2m deep brackish, shallow water basin areas of the Orbetello Lagoons have poor water circulation and considerable eutrophication phenomena. During MIVIS overflight, spectra of submerged vegetation (Cymodocea sp., Cladophora sp., Chaetomorpha sp., Gracilaria sp., Enteromorpha sp., Ruppia sp. and Ulva sp.) were collected from a boat equipped with a field portable multi-spectral radiometer operating between 380 and 780nm. In situ collected spectra and MIVIS spectra, in the visible and the near infrared region for prototype area, were compared to select the representative spectra of submerged vegetation. The Spectral Angle Mapper (SAM) has been the method adopted for the spectral classification.     

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.     

Inspecting MIVIS capability to retrieve chemical–mineralogical information: evaluation and analysis of VNIR–SWIR data acquired on a volcanic area

This work is a contribution to the assessment of MIVIS (Multi-spectral Infrared and Visible Imaging Spectrometer) airborne imaging spectrometer capability in applications of surface characterization. The focus is on the visible and near-infrared–short wave infrared (VNIR–SWIR) spectral region, using a dataset acquired in 1994 on Vulcano Island (Italy), to retrieve chemical–mineralogical information on the altered deposits related to volcanic activity. The main processing steps include data quality evaluation in terms of signal-to-noise ratio, atmospheric and topographic corrections and spectral interpretation of the image. Estimation of surface reflectance is based on atmospheric modelling by MODTRAN3.5 and 6S radiative transfer codes. Representative MIVIS reflectance spectra of the main surface units are compared with spectra measured in the laboratory on field samples, and interpreted to characterize the mineralogy on the basis of their spectral features. A thematic map of the main alteration units is then produced by applying spectral mapping techniques to the surface reflectance image, using a set of channels selected on the basis of their data quality and image-derived end-member spectra.