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A laboratory study on rare earth element bearing mine tailings, collected from Bangka Island, Indonesia, reported a new spectral absorption feature at 674 nm associated with Erbium. The present study aims to evaluate the capability of the European Space Agency’s Sentinel-2 MSI sensors to detect this absorption feature from space. An arithmetic band operation is performed on selected visible and near-infrared spectral bands of a Sentinel-2 image. The results show that Sentinel-2 MSI is capable of detecting the 674 nm Erbium-related absorption feature within the particular environmental setting of the study area.

A novel method is presented to measure rock microstructure in hyperspectral mineral maps of rock specimens. Shape parameters were calculated from rock objects in segmented mineral maps. Object area, object perimeter, object hull perimeter and fitted ellipses were used to calculate shape parameters such as compactness, convexity and a cookie-cutter parameter. Shape parameters were used to describe a variety of microstructures and microstructural elements. The parameters were tested on microstructures in artificial imagery and subsequently applied to hyperspectral mineral maps of rocks. Analyses of parameters calculated on artificial imagery showed that object shapes could be measured by the flattening of fitted ellipses as a measure of sphericity and elongation, together with the cookie-cutter parameters that measured angularity. Compactness and convexity could differentiate between euhedral, subhedral and anhedral crystal shapes. Aphanitic, phaneritic and porphyritic igneous microstructures could be identified and differentiated by homogeneity and relative object size parameters. The degree of sorting of sedimentary rocks was measured by the distribution of object sizes and statistical parameters describing the distribution. Orientation of single objects was measured by the angle between the major axis of a fitted ellipse and the vertical of the image. Preferred orientations in the rock microstructure were determined by calculation of a standardized resultant of orientation vectors and a mean angle. Layering and banding of the rock was identified by the length of major axes of fitted ellipses relative to the image dimension. The shape parameters calculated on objects in segmented hyperspectral mineral maps of rock specimens were able to discriminate between sedimentary and volcanic microstructures using the size distribution of mineral objects, the presence of a preferred orientation of the rock and a layered microstructure. The volcanic microstructures could be differentiated by the size distribution of amygdales, phenocrysts and xenocrysts in the rock. Shape parameters could be used to differentiate between xenocrysts and phenocrysts, the latter being more elongated in the studied samples. The study shows that object shape parameters can be used to measure microstructure and microstructural elements in mineral maps, and subsequently discriminate between different rock types and microstructures. The expression of microstructure into numeric parameters is a first step towards quantification of microstructures in mineral maps of rocks. Further development of the methodology could contribute to the creation of unbiased classification scheme of rocks, improved statistical modeling of compositional rock parameters such as mineral ore grades, and the automated recognition of microstructures in large image databases of rocks and drill-core.

We introduce a normalization algorithm which highlights short-term, localized, non-periodic fluctuations in hyper-temporal satellite data by dividing each pixel by the mean value of its spatial neighbourhood set. In this way we suppress signal patterns that are common in the central and surrounding pixels, utilizing both spatial and temporal information at different scales. We test the method on two subsets of a hyper-temporal thermal infra-red (TIR) dataset. Both subsets are acquired from the SEVIRI instrument onboard the Meteosat-9 geostationary satellite; they cover areas with different spatiotemporal TIR variability. We impose artificial fluctuations on the original data and apply a window-based technique to retrieve them from the normalized time series. We show that localized short-term fluctuations as low as 2K, which were obscured by large-scale variable patterns, can be retrieved in the normalized time series. Sensitivity of retrieval is determined by the intrinsic variability of the normalized TIR signal and by the amount of missing values in the dataset. Finally, we compare our approach with widely used techniques of statistical and spectral analysis and we discuss the improvements introduced by our method. HighlightsWe introduce a normalization approach for detection of extremes.We consider both the spatial and temporal dimensions of geophysical data.We apply the method and test its sensitivity on hyper-temporal satellite data.

Sentinel-2 is ESA's medium spatial resolution (10–60 m) super-spectral instrument aimed at ensuring data continuity for global land surface monitoring of Landsat and SPOT. Several simulation studies have been conducted that show the potential of Sentinel-2 for estimating biophysical and biochemical parameters such as leaf area index, chlorophyll and nitrogen, and spectral products such as the red edge position and NDVI time series, providing data continuity for a number of other operational sensors. This paper aims at simulating Sentinel-2 products that are relevant to the geology and soil science community and we compare these to well established band ratio products from ASTER. As the basis for the simulation we use airborne hyperspectral imagery from the HyMAP sensor which were spectrally and spatially resampled to the resolutions of ASTER and Sentinel-2 using actual spectral response functions and pixel aggregate, respectively. The simulated image products demonstrate a good correspondence between ASTER and Sentinel-2 VNIR and SWIR bands. A number of band ratios are proposed for Sentinel-2 to derive the following products: ferric iron, ferrous iron, laterite, gossan, ferrous silicate and ferric oxides. These are compared to established band ratios of real ASTER data as well as simulated (from HyMAP) ASTER data and they correlate favourably. Investigating the spatial patterns reveals that there is a good match between the proposed Sentinel-2 band ratio products and those of ASTER. The resulting band ratio products are compared to the local geologic map of the imaged hydrothermal area (the Rodalquilar mining area, Cabo de Gata volcanic field, SE Spain). It is shown that they support the existing conceptual geologic model of the epithermal deposit.

Spectral matching algorithms, such as the Spectral Angle Mapper (SAM), utilize the spectral similarity between individual image pixel spectra and a spectral reference library with known components. Here, we illustrate and quantify the effects that different sources of reference libraries have on SAM classification results. Synthetic images of three mineral endmembers were classified by using reference libraries derived from airborne hyperspectral imagery, ground spectra (Portable Infrared Mineral Analyser), and from a standard library (United States Geologic Survey). Results show that the source of the reference library strongly influences the classification results if all available wavelengths are used. This effect can be partially neutralized by using appropriate preprocessing methods. Two different types of spectral subsetting of the data, two types of continuum removal, and a combination thereof were tested. Best results were achieved by using a feature subset (i.e., limiting the input wavelengths to the diagnostic absorption features). This increased the average classification accuracy from 74% to 95% (ground spectral library) and from 68% to 94% (standard library).

The OMEGA instrument onboard of ESA's Mars Express mission is the first hyperspectral sensor that has collected data from Mars. The OMEGA team has shown that Mars has considerable surface compositional variation. Spectral interpretation and mineral mapping, however, is difficult on a pixel-by-pixel basis due to sensor noise, an atmosphere dominated by carbon dioxide and especially an unknown surface cover. An object- based segmentation approach is for datasets that are acquired in areas from which we do not have a-priori knowledge useful to ignore the scene-wide effects of the unknown atmosphere and to enhance the spectral contrast of the planet's surface, without any human bias. Unlike common segmentation procedures where distances in feature space are used for pixel similarity criteria, the OMEGA data is segmented using similarity criteria based on spectral absorption feature parameters such as position, depth and area. This paper shows the first results of an object-based processing of OMEGA data and discusses possibilities of future development.

This study presents a novel approach in combining textural information from the panchromatic band with spectral information from the multispectral bands for improved image classification. Firstly, we develop a texture measure based on wavelet coefficients of the panchromatic band that can be aggregated to the resolution of the multispectral bands. Secondly, we combine the texture measures with the spectral information in the multispectral bands in a fuzzy classification framework. Thirdly, we illustrate our approach with a case study of vegetation and land cover classification based on a Quickbird image of subantarctic Macquarie Island.

Object detection in remote sensing studies can be improved by incorporating spatial knowledge of an object in an image processing algorithm. This paper presents an algorithm based on sequential Hough transforms, which aims to detect botanical and mineralogical alterations that result from natural seepage of carbon dioxide and light hydrocarbons. As the observed alterations are not unique for gas seepages, these halos can only be distinguished from the background by their specific spatial pattern: the alterations are present as halos that line up along geological lineaments in the shallow subsurface. The algorithm is deployed in three phases: a prior spectral classification followed by two serialized Hough transforms. The first Hough transform fits circles through spectrally optimal matching pixels. Next, the centers of the detected circles are piped into the second Hough transform that detects points that are located on a line. Results show that our algorithm is successful in detecting the alteration halos. The number of false anomalies is sufficiently reduced to allow an objective detection based on field observations and spectral measurements.