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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Best Merge Region Growing with Integrated Probabilistic Classification for Hyperspectral Imagery

A new method for spectral-spatial classification of hyperspectral images is proposed. The method is based on the integration of probabilistic classification within the hierarchical best merge region growing algorithm. For this purpose, preliminary probabilistic support vector machines classification is performed. Then, hierarchical step-wise optimization algorithm is applied, by iteratively merging regions with the smallest Dissimilarity Criterion (DC). The main novelty of this method consists in defining a DC between regions as a function of region statistical and geometrical features along with classification probabilities. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana s vegetation area and compared with those obtained by recently proposed spectral-spatial classification techniques. The proposed method improves classification accuracies when compared to other classification approaches.

Tarabalka, Yuliya↗
NASA NTRS1998

Neural Network for Analysis of Hyperspectral Imagery

Each material has unique spectral distribution that can be viewed as its fingerprint. Advanced hyper and ultra spectral sensors will produce large amount of data. Therefore, advanced capabilities are required that can effectively deal with large data cubes, possibly in real time.

hyperspectral imagery neural network↗
DOE OSTI2025

One-shot gas detection with transformer paired neural networks in Mako collected longwave infrared hyperspectral imagery

To date, careful data treatment workflows and statistical detectors are used to perform hyperspectral image (HSI) detection of any gas contained in a spectral library, which is often expanded with physics models to incorporate different spectral characteristics. In general, surrounding evidence or known gas-release parameters are used to provide confidence in or confirm detection capability, respectively. This makes quantifying detection performance difficult as it is nearly impossible to develop an absolute ground truth for gas target pixel presence in collected HSI. Consequently, developing and comparing new detection methods, especially machine learning (ML)-based methods, is susceptible to subjectivity in derived detection map quality. Here, in this work, we demonstrate the first use of transformer-based paired neural networks (PNNs) for one-shot gas target detection for multiple gases while providing quantitative classification and detection metrics for their use on labeled data. Terabytes of training data are generated from a database of long-wave infrared HSI obtained from historical Mako sensor campaigns over Los Angeles. By incorporating labels, singular signature representations, and a model development pipeline, we can tune and select PNNs to detect multiple gas targets that are not seen in training on a quantitative basis. We additionally assess our test set detections using interpretability techniques widely employed with ML-based predictors, but less common with detection methods relying on learned latent spaces.

Hyperspectral imaging↗
NASA NTRS2005

Low-Complexity Lossless Compression of Hyperspectral Imagery via Adaptive Filtering

A low-complexity, adaptive predictive technique for lossless compression of hyperspectral data is presented. The technique relies on the sign algorithm from the repertoire of adaptive filtering. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.

Klimesh, M.↗
NASA NTRS2005

Low-Complexity Lossless Compression of Hyperspectral Imagery Via Adaptive Filtering

A low-complexity, adaptive predictive technique for lossless compression of hyperspectral data is presented. The technique relies on the sign algorithm from the repertoire of adaptive filtering. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.

lossless data compression↗
NASA NTRS2011

Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

Tarabalka, Yuliya↗
DOE OSTI2020

Monitoring of Terrestrial Aquatic Ecosystems Through Analysis of Time-Variant Hyperspectral Imagery

Time-variant spectral analysis of plants can classify stress-based perturbations (e.g., dry soil or increased salination) and quantify mechanistic attributes associated with plant photosynthesis, respiration, hormone function, and plant morphology. This project will investigate the utility of hyperspectral cameras for detecting and monitoring these perturbations.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2001

Nutrient Stress Detection in Corn Using Neural Networks and AVIRIS Hyperspectral Imagery

AVIRIS image cube data has been processed for the detection of nutrient stress in corn by both known, ratio-type algorithms and by trained neural networks. The USDA Shelton, NE, ARS Variable Rate Nitrogen Application (VRAT) experimental farm was the site used in the study. Upon application of ANOVA and Dunnett multiple comparsion tests on the outcome of both the neural network processing and the ratio-type algorithm results, it was found that the neural network methodology provides a better overall capability to separate nutrient stressed crops from in-field controls.

Estep, Lee↗
NASA NTRS2014

A Comparison of the Red Green Blue Air Mass Imagery and Hyperspectral Infrared Retrieved Profiles

The Red Green Blue (RGB) Air Mass imagery is derived from multiple channels or paired channel differences. Multiple channel products typically provide additional information than a single channel can provide alone. The RGB Air Mass imagery simplifies the interpretation of temperature and moisture characteristics of air masses surrounding synoptic and mesoscale features. Despite the ease of interpretation of multiple channel products, the combination of channels and channel differences means the resulting product does not represent a quantity or physical parameter such as brightness temperature in conventional single channel satellite imagery. Without a specific quantity to reference, forecasters are often confused as to what RGB products represent. Hyperspectral infrared retrieved profiles of temperature, moisture, and ozone can provide insight about the air mass represented on the RGB Air Mass product and provide confidence in the product and representation of air masses despite the lack of a quantity to reference for interpretation. This study focuses on RGB Air Mass analysis of Hurricane Sandy as it moved north along the U.S. East Coast, while transitioning to a hybrid extratropical storm. Soundings and total column ozone retrievals were analyzed using data from the Cross-track Infrared and Advanced Technology Microwave Sounder Suite (CrIMSS) on the Suomi National Polar Orbiting Partnership satellite and the Atmospheric Infrared Sounder (AIRS) on the National Aeronautics and Space Administration Aqua satellite along with dropsondes that were collected from National Oceanic and Atmospheric Administration and Air Force research aircraft. By comparing these datasets to the RGB Air Mass, it is possible to capture quantitative information that could help in analyzing the synoptic environment enough to diagnose the onset of extratropical transition. This was done by identifying any stratospheric air intrusions (SAIs) that existed in the vicinity of Sandy as the wind field expanded and the cloud pattern evolved into an atypical pattern.

Berndt, E. B.↗
NASA NTRS2014

A Comparison of the Red Green Blue (RGB) Air Mass Imagery and Hyperspectral Infrared Retrieved Profiles and NOAA G-IV Dropsondes

RGB air mass imagery is derived from multiple channels or paired channel differences. The combination of channels and channel differences means the resulting imagery does not represent a quantity or physical parameter such as brightness temperature in conventional single channel imagery. Without a specific quantity to reference, forecasters are often confused as to what RGB products represent. Hyperspectral infrared retrieved profiles and NOAA G-IV dropsondes provide insight about the vertical structure of the air mass represented on the RGB air mass imagery and are a first step to validating the imagery.

Berndt, Emily↗
NASA NTRS2022

QWIP: A Quantitative Metric for Quality Control of Aquatic Reflectance Spectral Shape using the Apparent Visible Wavelength

The colors of the ocean and inland waters span clear blue to turbid brown, and the corresponding spectral shapes of the waterleaving signal are diverse depending on the various types and concentrations of phytoplankton, sediment, detritus and colored dissolved organic matter. Here we present a simple metric developed from a global dataset spanning blue, green and brown water types to assess the quality of a measured or derived aquatic spectrum. The Quality Water Index Polynomial (QWIP) is founded on the Apparent Visible Wavelength (AVW), a one-dimensional geophysical metric of color that is inherently correlated to spectral shape calculated as a weighted harmonic mean across visible wavelengths. The QWIP represents a polynomial relationship between the hyperspectral AVW and a Normalized Difference Index (NDI) using red and green wavelengths. The QWIP score represents the difference between a spectrum’s AVW and NDI and the QWIP polynomial. The approach is tested extensively with both raw and quality controlled field data to identify spectra that fall outside the general trends observed in aquatic optics. For example, QWIP scores less than or greater than 0.2 would fail an initial screening and be subject to additional quality control. Common outliers tend to have spectral features related to: 1) incorrect removal of surface reflected skylight or 2) optically shallow water. The approach was applied to hyperspectral imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), as well as to multispectral imagery from the Visual Infrared Imaging Radiometer Suite (VIIRS) using sensor-specific extrapolations to approximate AVW. This simple approach can be rapidly implemented in ocean color processing chains to provide a level of uncertainty about a measured or retrieved spectrum and flag questionable or unusual spectra for further analysis.

remote sensing reflectance↗
DOE OSTI2023

FastACE

SAND2024-01893O The Fast Adaptive Cosine Estimator (FastACE) algorithm modifies the well-known Adaptive Cosine Estimator for target detection in hyperspectral imagery. Specifically, FastACE modifies the computation of the background precision matrix (C_b^-1) under a Vecchia approximation. That is, each spectral band, conditioned on a local neighborhood of bands around that band, is independent of the other spectral bands. The FastACE algorithm leverages a parameterizable, auto-regressive neighborhood for the conditional independence assumption. The underlying math used for computing detection scores is equivalent to ACE, but it is implemented within FastACE. The software implements a target detection algorithm and associated utilities for target detection in hyperspectral imagery. Provided with a hyperspectral image and corresponding target signature, it produces relevant background statistics and the corresponding detection scores for the target signature in the image. The software is designed to integrate into other end-user applications or processing. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

VanderLaan, John↗
NASA NTRS2005

A Parallel Processing Algorithm for Remote Sensing Classification

A current thread in parallel computation is the use of cluster computers created by networking a few to thousands of commodity general-purpose workstation-level commuters using the Linux operating system. For example on the Medusa cluster at NASA/GSFC, this provides for super computing performance, 130 G(sub flops) (Linpack Benchmark) at moderate cost, $370K. However, to be useful for scientific computing in the area of Earth science, issues of ease of programming, access to existing scientific libraries, and portability of existing code need to be considered. In this paper, I address these issues in the context of tools for rendering earth science remote sensing data into useful products. In particular, I focus on a problem that can be decomposed into a set of independent tasks, which on a serial computer would be performed sequentially, but with a cluster computer can be performed in parallel, giving an obvious speedup. To make the ideas concrete, I consider the problem of classifying hyperspectral imagery where some ground truth is available to train the classifier. In particular I will use the Support Vector Machine (SVM) approach as applied to hyperspectral imagery. The approach will be to introduce notions about parallel computation and then to restrict the development to the SVM problem. Pseudocode (an outline of the computation) will be described and then details specific to the implementation will be given. Then timing results will be reported to show what speedups are possible using parallel computation. The paper will close with a discussion of the results.

Gualtieri, J. Anthony↗
NASA NTRS2002

Low Altitude AVIRIS Data for Mapping Landform Types on West Ship Island, Mississippi

A chain of barrier islands provides protection against hurricanes and severe storms along the south and southeastern shores of the United States. Barrier island landform types can be spectrally similar and as small as a few meters across, making highly detailed maps difficult to produce. To determine whether high-resolution airborne hyperspectral imagery could provide detailed maps of barrier island landform types, we used low-altitude hyperspectral and multispectral imagery to map surface environments of West Ship Island, Mississippi. We employed 3.4-meter AVIRIS hyperspectral imagery acquired in July 1999 and 0.5-meter ADAR multispectral data acquired in November 1997. The data were co-registered to digital ortho aerial imagery, and the AVIRIS data was scaled to ground reflectance using ATREM software. Unsupervised classification of AVIRIS and ADAR data proceeded using ISODATA clustering techniques. The resulting landform maps were field-checked and compared to aerial photography and digital elevation maps. Preliminary analyses indicated that the AVIRIS classification mapped more landform types, while the ADAR-based map enabled smaller patches to be identified. Used together, these maps provided a means to assess landform distributions of West Ship Island before and after Hurricane Gorges. Classification accuracy is being addressed through photo-interpretation and field surveys of sample areas selected with stratified random sampling.

Spruce, Joseph↗