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Spectral unmixing-based estimation of material abundances in hyperspectral imagery has a variety of applications in mineralogy, environmental monitoring, agriculture, food processing, pharmacy, etc. A substantial body of literature is available on different inversion algorithms, optional pre-processing such as dimensionality reduction, and algorithms for endmembers extraction. The quality of abundance estimation depends on the number of materials, size, the geometrical orientation of materials, the source of endmembers, and the inversion algorithm used. However, there is a lack of studies on one-to-one assessment of the retrieval of abundances under various scenarios of spectral material distributions, the spatial resolution of the imagery, and the potential of in-situ reflectance measurements as candidate endmembers. The unavailability of comprehensive benchmark data coupled with pixel-to-pixel ground truth data has impeded comprehensive assessment of the first principles of spectral unmixing from a verifiable experimental perspective. The objective of this research is assessing the dynamics of material abundance as a function of the source of endmembers, spatial resolution, number of materials, and the size of materials. Linear and its sparse-based spectral unmixing algorithms were implemented on the datasets acquired for the estimation of abundances, considering the different scenarios of material distributions, spatial resolution, and the source of endmembers. We validated the results using pixel-to-pixel ground truth maps prepared for the different cases of spectral unmixing. The results provide answers to some critical open challenges in spectral unmixing, such as, (i) for an unambiguous detection, the fractional distribution of material has to be at least 1% of the pixel, (ii) endmembers from the in-situ spectra based on the external spectral library can offer reasonably good abundance estimates (an error of up to 20% compared to the image-based endmembers), and (iii) geometric orientations of materials in the ground sampling distance influence the abundance estimations. The benchmark dataset generated in this work is a valuable resource for addressing intriguing questions in spectral unmixing using hyperspectral imagery from a multi-resolution perspective. [ABSTRACT FROM AUTHOR]

Atmospheric correction is an important pre-processing step in various spatio-temporal and multi-sensor remote sensing data analyzes based applications. Absolute atmospheric corrections are carried out using physical based models, generally known as radiative transfer (RT) codes such as MODerate resolution atmospheric TRANsmission (MODTRAN), Second Simulation of the Satellite Signal in the Solar Spectrum (6S) etc. Most of the available atmospheric correction schemes are commercially off-the-shelf and use patented RT codes. The objective of the present work is to develop an open-end atmospheric correction scheme, named as Flexible Atmospheric Compensation Technique (FACT), based on open source 6S RT code. The proposed FACT scheme utilizes look-up architecture for simulating the outputs of 6S RT code for various input parameters' combination. Input parameters such as initial visibility, columnar water vapour are estimated using the dark object and the Continuum Interpolated Band Ratio (CIBR) methods respectively. The proposed FACT scheme has been evaluated exhaustively using spatio-spectral statistical error measures such as Spatial-Root Mean Square Error (S-RMSE), Spatial-Mean Absolute Error (S-MAE) and spectral-RMSE by comparing the performance with widely used Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) and the customized NASA JPL's atmospheric correction scheme. Datasets from hyperspectral (AVIRIS-NG and Hyperion) and multispectral (LANDSAT-8 OLI and WorldView-3) remote sensors were chosen for comparative analysis of the developed atmospheric correction scheme against other atmospheric correction schemes. Results confirm that the proposed FACT scheme offers accuracy of about 95% for hyperspectral imaging sensors and close to 98% for multispectral imaging sensors when compared with FLAASH. Despite marginal disagreements for certain land cover features at the water vapour absorbing spectral regions, we find the proposed FACT scheme a plausible option for carrying out absolute atmospheric correction of various operational remote imaging sensors. [ABSTRACT FROM AUTHOR]

Power management through dynamic core, cache and frequency adaptation is becoming a necessity in today’s power-constrained many-core environments. Unfortunately, as core count grows, the complexity of both the adaptation hardware and the power management algorithms increases exponentially. This calls for hierarchical solutions, such as on-chip voltage regulators per-tile rather than per-core, along with multi-level power management. As power-driven adaptation of shared resources affects multiple threads at once, the efficiency in a tile-organized many-core processor architecture hinges on the ability to co-schedule compatible threads to tiles in tandem with hardware adaptations per tile and per core. In this paper, we propose a two-tier hierarchical power management methodology to exploit per-tile voltage regulators and clustered last-level caches. In addition, we include a novel thread migration layer that (i) analyzes threads running on the tiled many-core processor for shared resource sensitivity in tandem with core, cache and frequency adaptation, and (ii) co-schedules threads per tile with compatible behavior. On a 256-core setup with 4 cores per tile, we show that adding sensitivity-based thread migration to a two-tier power manager improves system performance by 10% on average (and up to 20%) while using 4 × less on-chip voltage regulators. It also achieves a performance advantage of 4.2% on average (and up to 12%) over existing solutions that do not take DVFS sensitivity into account. [ABSTRACT FROM AUTHOR]

Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the quality of retrievals are functions of various parameters of the sensor, platform, target–background dynamics, targets' spectral contrast, and atmospheric influence. Generally, target detection in remote sensing imagery has been approached using various statistical detection algorithms with an assumption of linearity in the image formation process. Knowledge on the image acquisition geometry, and spectral features and their stability across different imaging platforms is vital for designing a spectral target detection system. We carried out an integrated target detection experiment for the detection of various artificial target materials. As part of this work, we acquired a benchmark multi-platform hyperspectral and multispectral remote sensing dataset named as 'Gudalur Spectral Target Detection (GST-D)' dataset. Positioning artificial targets on different surface backgrounds, we acquired remote sensing data by terrestrial, airborne, and space-borne sensors on 20th March 2018. Various statistical and subspace detection algorithms were applied on the benchmark dataset for the detection of targets, considering the different sources of reference target spectra, background, and the spectral continuity across the platforms. We validated the detection results using the receiver operation curve (ROC) for different cases of detection algorithms and imaging platforms. Results indicate, for some combinations of algorithms and imaging platforms, consistent detection of specific material targets with a detection rate of about 80% at a false alarm rate between 10−2 to 10−3. Target detection in satellite imagery using reference target spectra from airborne hyperspectral imagery match closely with the satellite imagery derived reference spectra. The ground-based in-situ reference spectra offer a quantifiable detection in airborne or satellite imagery. However, ground-based hyperspectral imagery has also provided an equivalent target detection in the airborne and satellite imagery paving the way for rapid acquisition of reference target spectra. The benchmark dataset generated in this work is a valuable resourcefor addressing intriguing questions in target detection using hyperspectral imagery from a realistic landscape perspective. [ABSTRACT FROM AUTHOR]