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检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery IV"

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SAGE iv Pathfinder multi-spectral imaging spectrometer telescope paves the way for semi-custom CubeSat imaging missions 25

SAGE IV Pathfinder multi-spectral imaging spectrometer teles...

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25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Halterman, Alexander Cheff Damadeo, Robert Hill, Charles Buleri, Christine Murchison, Luke Obland, Michael Phenis, Adam Yacoby, Shimshone Quartus Engn Inc 9689 Towne Ctr Dr San Diego CA 92121 USA NASA Langley Res Ctr LARC 1 NASA Dr Hampton VA 23666 USA AMP Opt LLC 13308 Midland RdUnit 1304 Poway CA 92074 USA

ISBN: (纸本)9781510626386

The SAGE iv (Stratospheric Aerosol and Gas Experiment) Pathfinder looks towards ushering in the next generation of the SAGE family of instruments, leveraging solar occultation to retrieve vertical profiles of aerosols and gases in the stratosphere, providing high precision calibration data for other instruments. A development funded through the NASA Earth Science Technology Office (ESTO) Instrument Incubator Program ( IIP) SAGE iv Pathfinder is designed to extend the data record from the SAGE III scanning grating spectrometer with a multispectral imaging approach. Solar disk imaging improves the data collected by providing: (1) absolute pointing information;(2) measurements of atmospheric refraction effects;and (3) measurements of solar disk anisotropy. This additional information relaxes traditionally tight constraints on attitude knowledge, stability, and pointing control making a free-flying 6U CubeSat instrument feasible. Early estimates show this approach might reduce the cost of SAGE continuity missions by as much as 90%. A key benefit of the SAGE iv Pathfinder design to future missions is the versatility of the resultant telescope subsystem. The F/5.25 telescope resulted in >90% encircled energy within a 30 mu m/28 arcsecond pixel and point source normalized irradiance transmittance (PSNIT) of <1E-4 0.5 degrees outside of the field of view (FOV). The baseline design can be adapted to accommodate changes to layout, aperture, focal lengths, filters, and/or detectors in various CubeSat form factors. The telescope was designed to be thermally agnostic, with STOP analysis results indicating negligible performance variation as thermal gradients fluctuate on orbit. Once thermal validation of STOP analysis is completed, proven micron-level alignment, mounting, and analyses can then be leveraged for new high performance, semi-custom instruments, saving significant development cost for future science missions.

关键词： SAGE CubeSat telescope imaging spectrometer NASA three-mirror anastigmat STOP analysis

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Geological mapping in Morozumi range and Helliwell hills areas, northern Victoria Land (NVL), Antarctica using remote sensing imagery 40

Geological mapping in Morozumi range and Helliwell hills are...

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40th Asian conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019

作者： Pour, Amin Beiranvand Park, Yongcheol Hong, Jong Kuk Muslim, Aidy M. Pradhan, Biswajeet Songdomirae-ro Yeonsu-gu Incheon21990 Korea Republic of Kuala Nerus Terengganu21030 Malaysia School of Systems Management and Leadership Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia

Many regions remain poorly studied in terms of geological mapping in inaccessible regions especially in the Arctic and Antarctica due to harsh conditions and logistical difficulties. Application of specialized image processing techniques is capable of revealing the hidden linearly mixed spectral sources in multispectral and hyperspectral satellite images. In this study, the application of Independent component analysis (ICA) and Constrained Energy Minimization (CEM) algorithms was evaluated for Landsat-8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for geological mapping in Morozumi Range and Helliwell Hills areas, Northern Victoria Land (NVL), Antarctica. ICA algorithm was able to detect hidden linearly mixed spectral sources and low probability target materials in Landsat-8 and ASTER datasets. Fraction images of endmember target minerals such as hematite, goethite, jarosite, alunite, kaolinite, muscovite, epidote, chlorite, calcite, quartz, opal and chalcedony were produced using CEM algorithm for two spatial subsets of ASTER scene covering the Morozumi Range and Helliwell Hills areas. CEM classification image maps indicated that chlorite/hematite, goethite/jarosite/calcite and kaolinite/muscovite are governed in the Morozumi Range and goethite, chlorite, hematite and epidote are most dominated mineral assemblages in the Helliwell Hills area. GPS survey and XRD analysis verified the alteration mineral assemblages detected by ICA and CEM image processing algorithms. The results of this investigation demonstrate the capability of the two algorithms in distinguishing pixel and subpixel targets in the multispectral satellite data. The application of the methods for identifying poorly exposed geologic materials and subpixel exposures of alteration minerals has invaluable implications for geological mapping and mineral exploration in inaccessible regions. © 2020 40th Asian conference on Remote Sensing, ACRS 2019: "Progre

关键词： Landsat

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Optimizing Deep Learning Model Selection for Angular Feature Extraction in Satellite imagery 25

Optimizing Deep Learning Model Selection for Angular Feature...

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25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Immel, Poppy Desai, Meera Moody, Daniela, I Ursa Space Syst New York NY 14850 USA Barnard Coll New York NY USA

ISBN: (纸本)9781510626386

Deep learning techniques have been leveraged in numerous applications and across different data modalities over the past few decades, more recently in the domain of remotely sensed imagery. Given the complexity and depth of Convolutional Neural Networks (CNNs) architectures, it is difficult to fully evaluate performance, optimize the hyperparameters, and provide robust solutions to a specific machine learning problem that can be easily extended to similar problems, e.g. via transfer learning. Ursa Space Systems Inc. (Ursa) develops novel machine learning approaches to build custom solutions and extract answers from Synthetic Aperture Radar (SAR) satellite data fused with other remote sensing datasets. One application is identifying the orientation with respect to true north of the inlet pipe, which is one common feature located on the top of a cylindrical oil storage tank. In this paper, we propose a two-phase approach for determining this orientation: first an optimized CNN is used to probabilistically determine a coarse orientation of the inlet pipe, followed by a maximum likelihood voting scheme to automatically extract the location of the angular feature within 7.5 degrees. We present a systematic technique to determine the best deep learning CNN architecture for our specific problem and under user-defined optimization and accuracy constraints, by optimizing model hyperparameters (number of layers, size of the input image, and dataset preprocessing) using a manual and grid search approach. The use of this systematic approach for hyperparameter optimization yields increased accuracy for our angular feature extraction algorithm from 86% to 94% and can be extended to similar applications.

关键词： Feature extraction Optimization (mathematics) Binary data Image classification Satellites Performance modeling Satellite imaging Remote sensing Convolutional neural networks

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Evaluation of super-resolution algorithms for mosaic hyperspectral imagery 4

Evaluation of super-resolution algorithms for mosaic hypersp...

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conference on Emerging Imaging and Sensing Technologies for Security and Defence iv part of SPIE Security + Defence Symposium

作者： Nieuwenhuizen, Robert Schottner, Michel Pruim, Raimon van Dijk, Roelof van de Stap, Nanda Schutte, Klamer TNO POB 96864 NL-2509 JG The Hague Netherlands

ISBN: (数字)9781510630307

ISBN: (纸本)9781510630307;9781510630291

hyperspectral imaging sensors acquire images in a large number of spectral bands, unlike traditional electro-optical and infrared sensors which sample only one or few bands. hyperspectral mosaic sensors acquire an image of all spectral bands in one shot. Using a patterned array of spectral filters they measure different wavelength bands at different pixel locations, but this comes at the cost of a lower spatial resolution, as the sampling per spectral band is lower. Software algorithms can compensate for this loss in spatial sampling in each spectral channel. Here we compare the image quality obtained with spatial bicubic interpolation and two categories of super resolution algorithms: two single frame super resolution algorithms which exploit spectral redundancies in the data and two multiframe super resolution algorithms which exploit spatio-temporal structure. We make a quantitative assessment of the spatial and spectral image reconstruction quality on synthetic data as well as on semi-synthetic mosaic sensor data for applications in security and medical domains. Our results show that multi frame super resolution provides the best spatial and signal-to-noise quality. The single frame super resolution approaches score lower on spatial sharpness but do provide a substantial improvement compared to mere spatial interpolation, while providing in some cases the best spectral quality.

关键词： hyperspectral imaging super resolution optical filters image processing reconstruction algorithms demosaicing performance evaluation computational imaging

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Water Wave Glint Corrections, Water Depth, Light Attenuation and WorldView-3 Remote Sensing algorithms for Indian River Lagoon

Water Wave Glint Corrections, Water Depth, Light Attenuation...

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conference on Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions held at SPIE Remote Sensing conference

作者： Bostater, Charles R., Jr. Aziz, Samin Florida Inst Technol Marine Environm Opt Lab Melbourne FL 32901 USA Florida Inst Technol Ctr Remote Sensing Dept Ocean Engn & Marine Sci Melbourne FL 32901 USA

ISBN: (数字)9781510630048

ISBN: (纸本)9781510630031;9781510630048

A water wave glint correction algorithm, light attenuation depths and water depths estimated from satellite imagery in the Indian River Lagoon and Banana River estuarine waters provide a means to evaluate light penetration or water clarity. The shallow water estuarine study area is along Florida's east coast area known as"space coast" Florida. In-situ data, including water depths obtained from bathymetric charts, in-situ hyperspectral attenuation signatures and satellite-based reflectance factors are obtained from atmospherically corrected visible and near infra-red channels of World View-3 satellite imagery. The high spatial, radiometric and 11bit digital resolution satellite imagery is used to study water visibility that is an important ecosystem quality indicator. In shallow coastal waters as well as deeper water areas and nearby coastal waterways, the effects of water surface gravity wave facets and sun glint influence the estimates of water quality related variables estimated at the pixel level. The results have significant implications related to the estimation of the depth of penetration of light using satellite-based methods in coastal waters. Application of atmospherically corrected satellite imagery followed by glint removal is shown using translating and dilating derivative wavelet algorithms to estimate the attenuation depth of light in water. The research has been conducted in order that results may be transferable to other estuaries that suffer from reduction in light penetration due to factors such as turbidity or harmful algal blooms.

关键词： water visibility hyperspectral multispectral spectral attenuation water quality radiative transfer Monte Carlo simulations satellite imagery glint corrections wavelet algorithms derivative spectroscopy atmospheric correction wave facet glint removal

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Comparison of Bad Pixel Replacement Techniques for LWIR hyperspectral imagery 24

Comparison of Bad Pixel Replacement Techniques for LWIR Hype...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Martin, Jacob A. Islas, Genesis RYMT Air Force Res Lab Wright Patterson AFB OH 45433 USA Arizona State Univ Tempe AZ 85287 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

This paper compares four different bad pixel replacement algorithms for LWIR hyperspectral imagery representing both physics-based unmixing and statistics-based methods. Testing is performed on a measured dataset using detection performance as a comparison metric and synthetic dataset with reconstruction error and detection performance used to compare. It is found that a statistics-based covariance matched filter method generally performs best of the four methods tested but at significantly greater computational cost. A simple plate metaphor interpolation is the fastest technique but struggles to correct pixels where sharp spectral or spatial difference are present. Meanwhile, an endmember-based unmixing approach provided a balance between the two in terms of computational complextity and reconstruction performance.

关键词： hyperspectral imaging Long wavelength infrared Detection and tracking algorithms Sensors Target detection hyperspectral target detection Reconstruction algorithms

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Underwater target detection with hyperspectral imagery for search and rescue missions 24

Underwater target detection with hyperspectral imagery for s...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Eken, Isa Cem Cetin, Yasemin Yardimci Middle East Tech Univ Dumlupinar Blvd 1 TR-06800 Cankaya Turkey

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Fast and precise target detection is an issue in underwater object detection applications such as search and rescue missions. hyperspectral imaging could be effective in determining different elements in a region thanks to its ability to provide detailed spectral information. Utilization of this rich spectral information calls for the analysis of spectral differences of objects on water surface (dry/wet) and underwater conditions. In our study, we investigated the effect of water column to the target spectrum with two water column correction models and evaluated the results obtained with these models in two detection algorithms. The results show that the methodology enhances underwater object detection performance. The materials used for the study are selected to be typical in search and rescue missions such as metal, cotton fabric with different colors and blue denim to evaluate the effect of these cases on the detectability of targets for such scenarios.

关键词： hyperspectral Underwater Target Detection Search and Rescue

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Seeing through heavily polluted satellite imagery using QUAC 24

Seeing through heavily polluted satellite imagery using QUAC

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Adler-Golden, Steven Bernstein, Lawrence St Peter, Benjamin Tannian, Bridget Spectral Sci Inc Burlington MA 01803 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Extremely thick haze caused by air pollution is observed in many satellite images of the earth, and in particular over eastern China Standard image display software typically provides satisfactory visualization of the ground through automated or user-driven scaling to enhance contrast;however, it does not perform well with these highly polluted scenes, where the haze is spatially non-uniform. Furthermore, estimation of surface reflectance using standard atmospheric correction software is highly problematic under these conditions due to very low visible transmission of the haze coupled with lack of knowledge of its optical properties, which may not conform to the haze or aerosol models in the software. In this paper we show that a version of the empirical Quick Atmospheric Correction (QUAC) algorithm, adapted for spatially dependent scattering, produces visually satisfying imagery of the entire ground in multispectral satellite scenes containing thick haze, and that the output reflectance spectra appear to be realistic enough for performing basic surface classification. The QUAC algorithm is applicable to multispectral and hyperspectral imagery with any number of wavelength bands, including true color (RGB) imagery, and does not require radiometrically calibrated data.

关键词： atmospheric correction multispectral image haze air pollution Planet Labs QUAC

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Onboard Cubesat Data Processing for hyperspectral Detection of Chemical Plumes 24

Onboard Cubesat Data Processing for Hyperspectral Detection ...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Theiler, James Foy, Bernard R. Safi, Claira Love, Steven P. Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

We describe the development and implementation of plume detection algorithms under severe bandwidth and processing constraints imposed by a CubeSat architecture. In particular, two ideas will be presented: one employs onboard processing to reduce the data that is downlinked, and one employs the Sparse Matrix Transform (SMT) to speed up the onboard computation of an approximate Mahalanobis distance.

关键词： cubesat satellite hyperspectral imagery algorithm plume detection sparse matrix transform Mahalanobis distance matched filter adaptive coherence estimator

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Dimensionality reduction for spatial-spectral target detection on hyperspectral imagery 24

Dimensionality reduction for spatial-spectral target detecti...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Kaufman, Jason R. Meola, Joseph Univ Dayton Res Inst 300 Coll Pk Dr Dayton OH 45469 USA Air Force Res Lab 2211 Avion Circle Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

hyperspectral images often contain hundreds of spectral bands. Man-made anti natural materials usually exhibit variability their reflective and emissive across these bands, which is exploitable via target detection algorithms. The high dimensional nature of hyperspectral data has driven studies that explored ways to reduce spectral dimensionality without adversely affecting spectral target detection. Recently, spatial-spectral feature extraction techniques have demonstrated additional discrimination capability versus spectral only approaches in VNIR, SWIR, and LWIR hyperspectral imagery. When spatial descriptors are applied to spectral bands within a hyperspectral image, the length of a resulting spatial-spectral feature vector can be several times that of the original spectrum. While numerous efforts to reduce the dimensionality of hyperspectral imagery have been undertaken, they have not been commonly extended to the spatial-spectral domain. In this work, we address the relatively new problem of spatial-spectral dimensionality reduction through a strategy designed to remove features that neither negatively affect a target detection algorithm's capability to detect targets nor detract from that algorithm's ability to discriminate between targets in an exemplar signature library.

关键词： hyperspectral imagery dimensionality reduction spatial-spectral feature extraction spatial-spectral target detection spectral target detection

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