Infrared Imaging Spectrometer Research Articles

Comparative analysis of AVIRIS-NG and Landsat-8 OLI data for lithological mapping in parts of Sittampundi layered complex, Tamil Nadu, India

The present study addresses a comparative analysis of the potentials of Airborne Visible and infrared imaging spectrometer -next generation (AVIRIS-NG) data, with respect to Landsat-8 Operational Land Imager (OLI) data for mapping of sporadic exposures of mafic cumulates within the anorthosite intrusion and delineation of major lithological units of Sittampundi layered complex. AVIRIS-NG and Landsat-8 OLI data were initially compared using scene-based relative signal-to-noise ratio (SNR) analysis. Subsequently, spectral anomaly maps were generated to map different lithological rock units using the constrained energy minimization (CEM) approach. Spectral maps derived using CEM were further filtered by low-pass filtering to control the false-positive result. Spectral angle mapper (SAM) classification technique was applied to analyze the suitability of AVIRIS-NG and Landsat-8 OLI data for lithological mapping. Confusion matrix analysis was carried out to assess the SAM classified maps. The first vertical derivative (FVD) map of magnetic anomaly and field evidence/geological exposures were used to validate the results. SNR analysis indicates the superior signal strength of AVIRIS-NG data than Landsat-8 OLI data. Moreover, combined analysis based on CEM and SAM of remote sensing data and FVD of magnetic anomaly reveals that the false-positive area indicating metagabbro/mafic cumulate is lower in AVIRIS-NG data than Landsat-8 OLI data. This is because of the relatively poor SNR, spectral and spatial resolutions of Landsat-8 OLI data; however, it can be good enough for mapping of metagabbro/mafic cumulate when AVIRIS-NG data is unavailable.

Detection and Analysis of Microplastics in Human Sputum.

Microplastic pollution is an emerging environmental problem, and little research has focused on its impact on the human body. Based on retrospective case series, the study required participants to fill out a questionnaire and provide sputum samples in order to investigate the presence of microplastics in human sputum and determine whether humans involuntarily inhale them. A total of 22 patients suffering from different respiratory diseases were recruited. We used an Agilent 8700 laser infrared imaging spectrometer and Fourier-transform infrared microscope to analyze sputum samples and evaluate microplastics in the respiratory tract. Remarkably, the size range of the method for detecting microplastics in our study is 20-500 μm. The results showed that 21 types of microplastics were identified, and polyurethane was dominant, followed by polyester, chlorinated polyethylene, and alkyd varnish, accounting for 78.36% of the total microplastics. Most of the aspirated microplastics detected are smaller than 500 μm in size (median: 75.43 μm; interquartile range: 44.67-210.64 μm). Microplastics are ubiquitous in all sputum, indicating that inhalation is a potential way for plastics to enter the human body. Additionally, the quantities of microplastic types in the respiratory tract are related to smoking, invasive examination, etc. (P < 0.05). This study sheds new light on microplastic exposure, which provides basic data for the risk assessment of microplastics to human health.

Hyperspectral image classification using Hyb-3D convolution neural network spectral partitioning

Hyperspectral image classification (HSIC) on remote sensing imaging has brought immersive achievement using artificial intelligence technology. In deep learning convolution neural networks (CNN), 2D-CNN, and 3D-CNN methods are widely used to classify the spectral-spatial bands of hyperspectral images (HSI). The proposed Hybrid 3D-CNN (H3D-CNN) model framework for deeper features extraction predicts classification accuracy in supervised learning. The model reduces the narrow gap between supervised and unsupervised learning and the complexity and cost of the previous models. The HSI classification analysis is carried out on real-world data sets of Indian pines Salinas datasets captured by Airborne visible, infrared imaging spectrometer (AVIRIS) sensors that performed superior classification accuracy results.

Analysis and Suppression of Stray Radiation in Uncooled Thermal Infrared Imaging Spectrometer

Analysis and Suppression of Stray Radiation in Uncooled Thermal Infrared Imaging Spectrometer

Seasonal and interannual drought responses of vegetation in a California urbanized area measured using complementary remote sensing indices

The effects of drought can manifest in vegetation across an array of physiological responses and time scales. In metropolitan areas, vegetation provides shading and cooling during hot and dry conditions, but these benefits can be reduced with drought. While many studies have evaluated interannual vegetation drought responses, these responses to drought can be expressed diversely across seasons, especially in cities that regularly experience seasonal drought (e.g., in Mediterranean climates). Here, we evaluated seasonal and interannual drought responses across the dominant types of urban trees and grasses in the Santa Barbara, California, USA metropolitan area, primarily using Landsat imagery acquired from 2010 to 2019 as well as repeat Airborne Visible Infrared Imaging Spectrometer - Classic (AVIRIS-C) imagery acquired 2013–2015. To track vegetation types, we produced a random forest classification from 4 m AVIRIS-Next Generation (AVIRIS-NG) imagery acquired in June 2014 (overall accuracy = 86%; kappa = 0.85), thresholding to >90% pure pixels for most vegetation types in the coarser time series imagery. We monitored drought response from Landsat imagery using the Normalized Difference Vegetation Index (NDVI) and the difference in land surface temperature (ΔLST) between vegetation and developed/impervious surfaces. We used AVIRIS-C to measure equivalent water thickness (EWT), comparing it with NDVI. During drought years, NDVI was lower and ΔLST was closer to zero. Changes in EWT revealed seasonal adjustments by vegetation that were not readily apparent in the NDVI. To show how drought response expression in vegetation can vary by season, drought duration, and urban vegetation type, we examined the correlations of both NDVI and ΔLST to the Standardized Precipitation Evapotranspiration Index (SPEI) calculated over a range of time spans. For most vegetation types, the strongest correlations of NDVI to SPEI and ΔLST to SPEI were during the summer, except for annual grass and turfgrass NDVI, which had the strongest correlations in the winter. In general, NDVI and ΔLST for annual grass were most often correlated with SPEI at spans <12 months, particularly for NDVI. By contrast, NDVI and ΔLST for trees and turfgrass were commonly also correlated with SPEI at spans ≥12 months, in addition to seasonal time spans of <12 months. This study demonstrates the benefits of using functionally and seasonally distinctive remote sensing variables (NDVI, ΔLST, and EWT) together to quantify changes in vegetation canopy condition during droughts.

Joint VSWIR-TIR retrievals of earth's surface and atmosphere

Remote spectroscopy of terrestrial ecosystems generally falls into one of two categories: UV, Visible and ShortWave InfraRed (VSWIR) methods measuring reflected solar illumination, and Thermal InfraRed (TIR) methods measuring radiation emitted from the Earth's surface. Both interpret the measured radiance with inversion algorithms that estimate surface reflectance, emissivity, surface temperatures, and the atmospheric state. However, the two regimes have traditionally used independent inversion methods. Here we present the first simultaneous inversion of the upwelling radiance spectrum from the UV through the VSWIR and the Thermal Infrared. Maximum A Posteriori estimation determines the surface and atmosphere state that is most consistent with measured radiance across the entire range. We find that the complementary information in the two intervals improves estimates of atmospheric properties, surface emissivity, and surface temperature. Posterior uncertainty is 40% lower in surface temperature and 60% in air temperature. Spurious correlations between atmospheric and surface state are also reduced, with 90% lower correlated uncertainty between total column water vapor and both surface and air temperature. Temperature-emissivity separation also improves, with a 33% reduction in correlated posterior uncertainty between the parameters. We demonstrate the method on coincident data from the Classic Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) operating in the VSWIR range, and the Hyperspectral Thermal Emission Spectrometer (HyTES) in the TIR range, during flights on NASA's high-altitude ER-2 aircraft. Measurements spanning 0.4 to 12 microns can improve estimates of surface and atmospheric properties, significantly advancing studies in terrestrial ecosystems, geology, agriculture, and urban management.

Lucy Mission to the Trojan Asteroids: Science Goals

Abstract The Lucy Mission is a NASA Discovery-class mission to send a highly capable and robust spacecraft to investigate seven primitive bodies near both the L4 and L5 Lagrange points with Jupiter: the Jupiter Trojan asteroids. These planetesimals from the outer planetary system have been preserved since early in solar system history. The Lucy mission will fly by and extensively study a diverse selection of Trojan asteroids, including all the recognized taxonomic classes, a collisional family member, and a near equal-mass binary. It will visit objects with diameters ranging from roughly 1 km to 100 km. The payload suite consists of a color camera and infrared imaging spectrometer, a high-resolution panchromatic imager, and a thermal infrared spectrometer. Additionally, two spacecraft subsystems will also contribute to the science investigations: the terminal tracking cameras will supplement imaging during closest approach and the telecommunication subsystem will be used to measure the mass of the Trojans. The science goals are derived from the 2013 Planetary Decadal Survey and include determining the surface composition, assessing the geology, determining the bulk properties, and searching for satellites and rings.

Lucy Mission to the Trojan Asteroids: Instrumentation and Encounter Concept of Operations

Abstract The Lucy Mission accomplishes its science during a series of five flyby encounters with seven Trojan asteroid targets. This mission architecture drives a concept of operations design that maximizes science return, provides redundancy in observations where possible, features autonomous fault protection, and utilizes onboard target tracking near closest approach. These design considerations reduce risk during the relatively short time-critical periods when science data is collected. The payload suite consists of a color camera and infrared imaging spectrometer, a high-resolution panchromatic imager, and a thermal infrared spectrometer. The mission design allows for concurrent observations of all instruments. Additionally, two spacecraft subsystems will also contribute to the science investigations: the Terminal Tracking Cameras will obtain wide field-of-view imaging near closest approach to determine the shape of each of the Trojan targets and the telecommunication subsystem will carry out Doppler tracking of the spacecraft to determine the mass of each of the Trojan targets.

Using mobile surface in situ and remote sensing and airborne remote sensing to derive emissions from a producing central California oil field in complex terrain

In this study, we demonstrate how under certain wind conditions, a mobile surface in situ platform, Truck MObile trace Gas (TMOG) Surveyor, that includes a ceilometer to derive boundary layer height (BL) can derive emissions. Field in situ and airborne thermal infrared imaging spectroscopy data were acquired by the thermal infrared imaging spectrometer, Mako, for three adjacent, central California producing oil fields (Kern River, Kern Front, Poso Creek) on 14 Sept. 2018. Derived methane (CH4) and carbon dioxide (CO2) emissions from TMOG data were 46.6 ± 15 Mg dy−1 (17 ± 5.6 Gg yr−1) and 3.12 ± 1.02 Mg dy−1 (1140 ± 230 Gg yr−1), respectively, with about half the CH4 from near-field point-source plumes and half from a well-mixed far field plume. Uncertainty was 33 % from the Monte Carlo simulations. Mako data confirmed that emissions were in the far field and there were no strong, buoyant plumes. The survey vertically profiled a nearby mountain to measure winds at the boundary layer top and validate BL using in situ data.The CH4 to CO2 emissions ratio for plume matched reservoir composition to better than 10 %. The plume anomaly CH4 to ethane emissions ratio suggested ethane emissions of around 200 kg dy−1 (74 Mg yr−1). Mako data in 2015 and concurrent data in 2018 confirmed that most emissions were from distant plumes in the Poso Creek and Kern Front fields.

Detecting heat-inducing urban built-up surface material with multi remote sensing datasets using reflectance and emission spectroscopy

This study aims to evaluate the spatial and spectral characteristics of dominant heat-inducing urban built-up surface materials (UBSM) in Kalaburagi, Karnataka State (India) using multi remote sensing datasets and field/lab instruments. A total of 16 UBSM were collected at different locations of the study area and spectra were generated using ASD Field spec 3 Spectro radiometer operated at VNIR and SWIR regions (0.35 μm to 2.5 μm). The collected spectra were then resampled with the spectral range of 0.3–2.5 μm and used for classifying dimensionally reduced AVIRIS-NG (Airborne Visible and Infrared Imaging Spectrometer – Next Generation) dataset through Spectral Angle Mapper (SAM) classifier for understanding spatial and spectral characteristics of heat inducing UBSM in the study area. The classification shows that concrete, basalt stones and asbestos are the dominant UBSM in the study area and its spectral reflectance are comparatively low in the VNIR and SWIR regions. The land surface temperature (LST) of the study area was retrieved from the Landsat-8 TIRS (Thermal Infrared Sensor), and compared with the ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) LST data product and Fluke Infrared thermometer measurements operated at LWIR (8–14 μm). All these LST estimation and comparison processes were synchronized at the most possible manner (±7 days). Additionally, emissivity of collected UBSM were determined using FTIR (Fourier Transform Infrared Radiometer) emissivity in the laboratory. The amalgamation of results shows that barren land (mostly basaltic), basalt stones and concrete surfaces cover dominantly the high LST region of the study area. The FTIR measurements reveal that, among 16 dominant UBSM, basalt stones had recorded high spectral radiance (0.0309 Wm−1μm−1sr−1) and emissivity (0.98) followed by concrete (emissivity = 0.94). Whereas marble had the lowest spectral radiance (0.0246 Wm−1μm−1sr−1) and emissivity (0.49). The results agree with the SWIR reflection spectra (where basalt stones, concrete and barren rocks shows lower reflectance) and principle of Wien's displacement law (where radiance spectra display series of peaks that corresponding to the temperature measurements). Besides proposing a newer methodology for understanding spatial and spectral distribution of dominant heat inducing urban built-up surface materials, the results offer baseline data for studying microclimatic variations in the study area.

Multi-season unmixing of vegetation class fractions across diverse Californian ecoregions using simulated spaceborne imaging spectroscopy data

Spaceborne imaging spectrometers are expected to facilitate regional-scale vegetation analyses with multi-season hyperspectral imagery. However, we still lack a better understanding on both whether multi-season hyperspectral approaches are favorable over single-season approaches, as well as on the benefits of hyperspectral compared to multispectral data. Our study investigates the potential of multi-season unmixing of simulated Environmental Mapping and Analysis Program (EnMAP) data for vegetation class fraction mapping across diverse natural and semi-natural ecoregions in California, USA. We utilized spring, summer and fall 2013 simulated EnMAP imagery derived from Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) data covering study sites in the San Francisco Bay Area, Lake Tahoe and Santa Barbara. Regression-based unmixing with synthetic training datasets from spectral libraries was implemented for mapping needleleaf tree, broadleaf tree, shrub, herbaceous and non-vegetation fractions, and independent reference data was used for validation. Multi-season unmixing of simulated EnMAP had average Mean Absolute Errors (MAE) over all classes of 8.7% for the Bay Area, 8.5% for Lake Tahoe and 9.6% for Santa Barbara. However, larger errors in the low and high end of the fraction range remained, particularly in open-canopy woodlands and xeric shrub-dominated regions. Single-season unmixing of simulated EnMAP revealed large seasonal and regional variations within individual vegetation classes. In most cases, the best performing single-season unmixing had similar errors as the multi-season unmixing, i.e., ∆MAEs within ±1.0%. This points to the advantage of the multi-season integration strategy for more robust and generalized mapping independent from season and study site. Relative to EnMAP analyses, multi-season unmixing of multispectral Landsat composites for the same seasons yielded increases in average MAEs of +1.7%, +2.3% and +1.4% for the three study sites. This indicates that the higher spectral resolution of simulated EnMAP provides more relevant discriminative information when comparing contemporary image pairs. Unmixing of seasonal spectral-temporal metrics (STMs) from all available Landsat images for an entire year took advantage of the full temporal detail provided by these ongoing missions. We found Landsat STMs to effectively map vegetation class fractions, with average MAEs of 9.9%, 10.0% and 9.7% for the three study sites. Still, improvements particularly for mapping fractions of the woody vegetation classes through multi-season unmixing of simulated EnMAP point to the benefit of high spectral resolution data, and we assume that a comparable higher temporal resolution of hyperspectral satellites will further positively influence results. Overall, we conclude that multi-season unmixing of spaceborne imaging spectroscopy data holds great potential for advancing vegetation class fraction mapping across natural and semi-natural ecosystems.

Monitoring the rapid changes in mangrove vegetation of coastal urban environment using polynomial trend analysis of temporal satellite data

Over the last few decades, anthropogenic activities have triggered the rate of change in the function of mangrove ecosystems in coastal urban areas. Satellite imagery provides valuable information for mangrove mapping and monitoring. Metrics derived from the linear regression analysis of spectral indices (SIs) derived from satellites are commonly used for change analysis. This study examines the robustness of the widely used SIs derived from Landsat satellite image to distinguish mangroves and non-mangrove features and identify the non-linear changes over mangrove forest using the polynomial trend analysis. Airborne Visible and Infra-Red Imaging Spectrometer Next Generation (AVIRIS-NG) data was resampled to simulate the spectral-response of the Landsat sensor. One-way analysis of variance (ANOVA) and mutual information (MI) were applied to the simulated data to identify the optimal SIs. Based on the statistical analysis, modular mangrove recognition index (MMRI), modified normalized difference water index (MNDWI) and normalized difference built-up index (NDBI) were identified to delineate the mangrove, inundated and built-up region. Finally, time-series profiles of the identified spectral indices were generated using Landsat-8 (L8) and Landsat-7 (L7) data to analyse the change dynamics of the mangrove vegetation from 2002 to 2019. The proposed methodology was applied to study the changes in mangroves in the coastal areas of Kochi (Ernakulam District, Kerala State, India). The temporal-non-linear changes in the mangrove area were identified based on polynomial regression that revealed abrupt decline (42% decrease) in the mangrove area due to large-scale infrastructure development projects. The proposed approach is easy to implement, which enables the frequent monitoring of extensive mangrove forests.

Detection and quantification of CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; plumes using the WFM-DOAS retrieval on AVIRIS-NG hyperspectral data

Abstract. Methane is the second most important anthropogenic greenhouse gas in the Earth's atmosphere. To effectively reduce these emissions, a good knowledge of source locations and strengths is required. Airborne remote sensing instruments such as the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) with meter-scale imaging capabilities are able to yield information about the locations and magnitudes of methane sources. In this study, we successfully applied the weighting function modified differential optical absorption spectroscopy (WFM-DOAS) algorithm to AVIRIS-NG data measured in Canada and the Four Corners region. The WFM-DOAS retrieval is conceptually located between the statistical matched filter (MF) and the optimal-estimation-based iterative maximum a posteriori DOAS (IMAP-DOAS) retrieval algorithm, both of which were already applied successfully to AVIRIS-NG data. The WFM-DOAS algorithm is based on a first order Taylor series approximation of the Lambert–Beer law using only one precalculated radiative transfer calculation per scene. This yields the fast quantitative processing of large data sets. We detected several methane plumes in the AVIRIS-NG images recorded during the Arctic-Boreal Vulnerability Experiment (ABoVE) Airborne Campaign and successfully retrieved a coal mine ventilation shaft plume observed during the Four Corners measurement campaign. The comparison between IMAP-DOAS, MF, and WFM-DOAS showed good agreement for the coal mine ventilation shaft plume. An additional comparison between MF and WFM-DOAS for a subset of plumes showed good agreement for one plume and some differences for the others. For five plumes, the emissions were estimated using a simple cross-sectional flux method. The retrieved fluxes originated from well pads, cold vents, and a coal mine ventilation shaft and ranged between (155 ± 71) kg (CH4) h−1 and (1220 ± 450) kg (CH4) h−1. The wind velocity was a significant source of uncertainty in all plumes, followed by the single pixel retrieval noise and the uncertainty due to atmospheric variability. The noise of the retrieved CH4 imagery over bright surfaces (&gt;1 µW cm−2 nm−1 sr−1 at 2140 nm) was typically ±2.3 % of the background total column of CH4 when fitting strong absorption lines around 2300 nm but could reach over ±5 % for darker surfaces (&lt; 0.3 µW cm−2 nm−1 sr−1 at 2140 nm). Additionally, a worst case large-scale bias due to the assumptions made in the WFM-DOAS retrieval was estimated to be ±5.4 %. Radiance and fit quality filters were implemented to exclude the most uncertain results from further analysis mostly due to either dark surfaces or surfaces where the surface spectral reflection structures are similar to CH4 absorption features at the spectral resolution of the AVIRIS-NG instrument.

Combining simulated hyperspectral EnMAP and Landsat time series for forest aboveground biomass mapping

Forest aboveground biomass (AGB) is a critical measure of ecosystem structure and plays a key role in global carbon cycling. Due to its widespread availability, optical remotely sensed data are key for regional- and global-scale AGB assessment, and with the planned and recent launches of spaceborne imaging spectroscopy missions such as the Environmental Mapping and Analysis Program (EnMAP), understanding the benefit of added spectral information for AGB mapping is important. We used simulated EnMAP imagery derived from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery acquired over Sonoma County, California, USA in combination with Landsat time series to map forest AGB. A Gaussian Process Regression model was implemented to estimate forest AGB from one- and two-date (April and June) EnMAP imagery. A lidar-based reference AGB map was used as base data for training and validation sample extraction. As a comparison, we used corresponding Landsat Best Available Pixel (BAP) composites as well as a year-long 16-day interpolated Landsat time series (TS) for 2013. EnMAP imagery was able to effectively map forest AGB, with the two-date model (RMSE = 97.5 Mg/ha) outperforming the two single date models. All EnMAP models outperformed the corresponding Landsat BAP models, the best of which was the two-date model (RMSE = 108.8 Mg/ha). The added temporal dimension of the Landsat time series resulted in the best Landsat-based AGB map (RMSE = 102.3 Mg/ha). Combining the two datasets further improved AGB mapping efforts, with 2-date EnMAP + 2013 Landsat TS providing the best overall AGB maps (RMSE = 86.0 Mg/ha). This study demonstrates not only the added value of hyperspectral imagery for forest AGB mapping, but also the possible synergies between hyperspectral and multispectral data sources and hence between spectrally and temporally dense information. It can be expected that with the next generation of spaceborne hyperspectral sensors, the combination of dense spectral and temporal data will work to further improve global efforts for mapping forest AGB from optical Earth observation data.

Spectroscopic Imaging System in Mid-Wave Infrared Imaging Spectrometer on Geostationary Orbit

Spectroscopic Imaging System in Mid-Wave Infrared Imaging Spectrometer on Geostationary Orbit

Optical Design and Investigation of a Dual-Interference Channels and Bispectrum Static Fourier-Transform Imaging Spectrometer Based on Stepped Micro-Mirror

We demonstrate a novel tempo-spatial mixed-modulation Fourier-transform infrared imaging spectrometer. The static interference channel based on a stepped micro-mirror contributes a compact system with high stability. The instrument functions in two spectral bands ranging between 3.7- 4.8 μm and 7.7-9.3 μm, and the spectral resolution of the dual-interference channels achieves 51.5 and 4.1 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , respectively. The instantaneous field of view is 0.15 mrad, and the working F/1.93 enables high optical throughput. The optical design and tolerance are presented with experimental investigation results, showing attractive potential in remote sensing application.

Ecological assessment of Hooghly River considering a few of the more perturbed sites based on some relevant physico-chemical and biological variables—A part of the AVIRIS-NG (NASA-ISRO) ground truth verification

A study was performed on a 32 Km stretch of the River Hooghly as part of a project under the Airborne Visible Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) scheme between Indian Space Research Organization (ISRO) and National Aeronautics and Space Administration (NASA). It was governed by 33 water quality variables comprising physical, chemical, biological and ecological ones. The selected stations were within the tidal realm of the estuary, constituting the lower-middle stretch of the river that was severely affected by anthropogenic perturbations. Chief biotic components studied were phytoplankton population density, primary and auxiliary phytopigments, and productivity in addition to chromophoric dissolved organic matter (CDOM). Majority of the study sites were observed with very low concentrations of dissolved oxygen along with significant shifts in primary production. Overall prevalence of Cyanophyceae and Bacillariophyceae was indicative of significant eutrophication. Reverse phase high performance liquid chromatography (HPLC) and radiometry revealed the presence of a large array of algal taxonomic groups through the detection of various primary and auxiliary phytopigments. The data and the study itself is of paramount importance being part of the ground truth verification initiative which will be used to render remote sensing techniques more precise in the future to better monitor the estuarine system.

An efficient lossless compression technique for remote sensing images using segmentation based band reordering heuristics

ABSTRACT The size of remote sensing (RS) image is indeed massive due to hundreds capturing wavelengths bands used for collecting information about the ground surface. The data in the RS images are often redundant spatially, spectrally and temporally which thus give a sufficient prospect for compressing the images in different domains. In this paper, we propose a band reordering scheme to be applied on the segmented subgroup datasets of an original RS image dataset towards presenting a hybrid lossless compression technique. The proposed dataset segmentation-based band reordering process employs two heuristics, i.e. consecutive continuity limit of previous bands and continuity breakdown thresholds to be used for efficient coding of reordered dataset towards attaining better on-board compression. The aim of the proposed compression strategy is thus to increase compression gain through reordering each highly correlated bands’ segment in the RS data-cube for efficient coding. The performance of the proposed band reordering method is measured using the Consultative Committee for Space Data Systems (CCSDS) recommended lossless CCSDS 123.0-B-1 and three-dimensional (3D) Context-based Adaptive Lossless Image Codec (CALIC) predictors. The experimental results illustrate substantially higher compression of the proposed method than that of the state-of-the-art standards using various real RS hyperspectral and hyperspectral sound datasets. Quantitatively, the compression performances are increased by 0.10%–0.66% using CCSDS and 0.65–2.52% using 3D-CALIC for hyperspectral Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) datasets, 0.26%–1.00% using CCSDS and 2.16–2.57% using 3D-CALIC for hyperspectral Hyperion datasets and 4.61%–7.36% using CCSDS and 4.05%–4.88% using 3D-CALIC for hyperspectral sounder datasets by the proposed compression technique. Besides, the proposed compression scheme outperforms state-of-the-art investigated compression methods.

Synergistic use of ASTER, L-band ALOS PALSAR, and hyperspectral AVIRIS-NG data for exploration of lode type gold deposit – A study in Hutti Maski Schist Belt, India

In this study, we have explored the utility of advanced hyperspectral, dual-polarized microwave, and advanced multispectral data to delineate potential areas of mineralization associated with the lode type gold deposit in Hutti-Maski Schist Belt Karnataka, India. We derived different image-enhanced products using visible-near-infrared (VNIR) and short-wave infrared (SWIR) spectral bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to delineate major rock types of gold-bearing greenstone belt such as metabasalt, amphibolite, younger alkali feldspar rich granite, granodiorite, and tonalitic-granodioric gneiss (TTG). The principal component (PC) image composite of ASTER VNIR-SWIR bands was not only used to distinguish different granitoids but also confirmed their sharp instructive nature of contact with the greenstone rocks. We also used an index image composite using ASTER SWIR bands to discriminate granitoid, basalt and amphibolite. We could demarcate few lineaments extending through the mineralized zone (e.g., Hutti, Heera-Buddine mines, etc.) using the fused products of ASTER and two polarization channels of the Phased-array L band Synthetic Aperture Radar (PALSAR) sensor of the Advanced Land Observation Satellite (ALOS). All these image-enhanced products are validated using the reference geological map of the study area and field data collected along selected field -traverses. The mineralogical and petrographical data of the altered rocks from the study site revealed the formation of calcite, scapolite, and other minerals. Represenattive reflectance spectrum of the altered rock was used to spatially delineate the surface distribution of the patchy and small alteration zones using airborne hyperspectral data of the Advanced Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) sensor. In this regard, a unified spectral map of altered rocks was derived by integrating two alteration maps prepared independently using two different partial sub-pixel algorithms (constrained energy minimization and matched filtering). Occurrences of altered rocks in the vicinity to favorable geological lineaments and their spatial proximity to metavolcanic-younger felsic intrusive contacts suggest their importance as the potential sites for detailed exploration.

Quantitative Analysis of Mixed Pixels in Hyperspectral Image Using Fractal Dimension Technique

In this paper, a novel approach for identification of the components of a mixed pixel along with the quantitative analysis of the determined components of hyperspectral data has been proposed. The overall method is divided into three phases. First, the spectral sensitivity curves (SSCs) of the identified endmembers soil, water and vegetation using N-FINDR algorithm are generated. Next, the percentage of the mixture of the endmembers is computed for calculating the fractal dimension (FD). Afterward, the FD of SSCs is utilized for determining the mathematical model of the obtained curve. In order to verify that the adjacent pixels belong to the class identified using N-FINDR algorithm, two algorithms, i.e., spectral angle mapper and spectral information divergence, are employed for determining the spectral similarity of the mixed pixel with the reference spectrum. Lastly, the quantitative analysis of the SSC of the mixed pixel is performed by using the corresponding calculated FD in the derived mathematical model. The performance of the mathematical model is evaluated on the airborne visible infrared imaging spectrometer image data, and the outcome shows that the proposed framework achieves very promising results with a small number of mixed pixel samples.