Eyal Ben Dor

Mineral Classification of Land Surface Using Multispectral LWIR and Hyperspectral SWIR Remote-Sensing Data. A Case Study over the Sokolov Lignite Open-Pit Mines, the Czech Republic

Remote-sensing techniques offer an efficient alternative for mapping mining environments and assessing the impacts of mining activities. Airborne multispectral data in the thermal region and hyperspectral data in the optical region, acquired with the Airborne Hyperspectral Scanner (AHS) sensor over the Sokolov lignite open-pit mines in the Czech Republic, were analyzed. The emissivity spectrum was calculated for each vegetation-free land pixel in the longwave infrared (LWIR)-region image using the surface-emitted radiation, and the reflectance spectrum was derived from the visible, near-infrared and shortwave-infrared (VNIR–SWIR)-region image using the solar radiation reflected from the surface, after applying atmospheric correction. The combination of calculated emissivity, with the ability to detect quartz, and SWIR reflectance spectra, detecting phyllosilicates and kaolinite in particular, enabled estimating the content of the dominant minerals in the exposed surface. The difference between the emissivity values at λ = 9.68 µm and 8.77 µm was found to be a useful index for estimating the relative amount of quartz in each land pixel in the LWIR image. The absorption depth at around 2.2 µm in the reflectance spectra was used to estimate the relative amount of kaolinite in each land pixel in the SWIR image. The resulting maps of the spatial distribution of quartz and kaolinite were found to be in accordance with the geological nature and origin of the exposed surfaces and demonstrated the benefit of using data from both thermal and optical spectral regions to map the abundance of the major minerals around the mines.

A new approach for thresholding spectral change detection using multispectral and hyperspectral image data, a case study over Sokolov, Czech republic

Change detection and multitemporal analyses aim to detect changes occurring over a specific geographical area using two or more images acquired at two or more different times. In this article, we present a new thresholding approach for unsupervised change detection. This approach focuses on determining the threshold that discriminates between change and no-change pixels. The differences between pixels in the two images are associated with real changes or noise. We propose a thresholding scheme that separates the threshold into two parts: (1) a spectral domain threshold that accounts for errors related to sensor stability, atmospheric conditions, and data-processing variations, and (2) a spatial domain threshold associated with georectification errors. We demonstrate our method using both multispectral Landsat images and airborne imaging spectroscopy HyMap images. The results show that the spectral domain threshold gives high detection capabilities with moderate false-alarm rate. Adding the spatial domain threshold to the spectral domain threshold reduces the false-alarm rates while maintaining good detection capabilities.

Quantitative Detection of Settled Dust Over Green Canopy Using Sparse Unmixing of Airborne Hyperspectral Data

The main task of environmental and geoscience applications is efficient and accurate quantitative classification of earth surfaces and spatial phenomena. In the past decade, there has been a significant interest in employing hyperspectral unmixing (HU) to retrieve accurate quantitative information latent in hyperspectral imagery data. Recently, the ground-truth and laboratory measured spectral signatures promoted by advanced algorithms are proposed as a new path toward solving the unmixing problem of hyperspectral imagery in semisupervised fashion. This paper suggests that the sensitivity of sparse unmixing techniques provides an ideal approach to extract and identify dust settled over/upon green vegetation canopy using hyperspectral airborne data. Among the available techniques, this study presents the results of seven selected algorithms: 1) non-negative matrix factorization (NMF); 2) L1 sparsity-constrained NMF (L1_NMF); 3) L1/2 sparsity-constrained NMF (L1/2_NMF); 4) graph regularized NMF (G_NMF); 5) structured sparse NMF (SS_NMF); 6) alternating least-square (ALS); and 7) Lins projected gradient (LPG). The performance is evaluated on real hyperspectral imagery data via detailed experimental assessment. The results compared with performances of selected conventional unmixing techniques.

Spectral quality indicators for hyperspectral data

A novel approach to estimating at-sensor hyperspectral (HRS) data quality Q/A of Q/I is proposed. As the HRS sensors performance may vary in time and space, a method to assess at-sensor radiance values is necessary. In fact, vicarious calibration solutions usually rely on natural, well-known, bright and dark targets that are large in size and spectrally/radiometrically homogeneous. Since such targets are not commonly found in the field for every mission and their spectral features can sometimes resemble artifacts in the corrected radiance, a new vicarious calibration approach is needed. This paper is based on our new method Supervised Vicarious Calibration (SVC) that uses artificial agricultural black polyethylene nets of various densities as vicarious calibration targets that are set up along the airplanes trajectory (preferably near the airfield). The different densities of the nets combined with any bright background afford full coverage of the sensors dynamic range. We show that these artificial targets can be used to assess at-sensor radiance data quality within a short time by two suggested indicators named Rad/Ref (at-sensor Radiance divided by ground truth Reflectance) and RRDF (Radiance to Reflectance difference factor). Its enables gaining immediate Q/A of Q/I information on the acquired data, prior to completion of the campaign, which could save on flight hours, effort and resources in the case of a radiometrically miscalibrated sensor. Several case studies are presented using AISA-Dual sensor data taken at different times and locations. We demonstrate the performance of the suggested indicators in both spectral and spatial domains and discuss their limitation.

Mineral mapping of makhtesh ramon in israel using hyperspectral remote sensing day and night LWIR images

Hyperspectral remote sensing in the thermal infrared region has been acknowledged as an innovative tool for earth environmental studies that complements the optical spectral region. The current study focuses on mapping surface mineral content using day and night airborne data in the longwave infrared (LWIR) spectral region over a well-known mineralogical site in Israel. Data were acquired with the AisaOWL hyperspectral sensor over Makhtesh Ramon in the Negev desert in southern Israel. Major minerals could be identified by locating similarities in day and night atsensor radiance spectra. The analysis resulted in the classification of quartz, carbonates, gypsum, kaolinite and other silicates according to their observed spectral features in both day and night data.

Supervised Vicarious Calibration (SVC) of hyperspectral remote-sensing data

A full-chain process approach to extracting reflectance information from hyperspectral (HRS) data which is valid for all sensor qualities is proposed. This method is based on a mission-by-mission approach, followed by a unique vicarious calibration stage. As the HRS sensors performance may vary in time and space, a vicarious calibration method to retrieve accurate at-sensor radiance values is necessary. In fact, vicarious calibration solutions usually rely on natural, well-known, bright and dark targets that are large in size and radiometrically homogeneous. Since such targets are not commonly found in the field for every mission and their spectral features can sometimes resemble artifacts in the corrected radiance, a new vicarious calibration approach is needed. This paper describes a new method that uses artificial agricultural black polyethylene nets of various densities as vicarious calibration targets that are set up along the airplanes trajectory (preferably near the airfield). The different densities of the nets combined with any bright background afford full coverage of the sensors dynamic range. We show that these artificial targets can be used to assess data quality and correct at-sensor radiance within a short time. Several case studies are presented using AISA-Dual sensor data taken at different times from different locations. We term the suggested vicarious calibration approach Supervised Vicarious Calibration (SVC) and demonstrate its performance in term of spectral accuracy.