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NASA Technical Reports Server (Ntrs) 20130011237: onboard Algorithms for data Prioritization and Summarization of Aerial Imagery

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2016年

NASA Technical Reports Server (Ntrs) 20130011237: onboard Algorithms for data Prioritization and Summarization of Aerial Imagery by NASA Technical Reports Server (Ntrs); NASA Technical Reports Server (Ntrs); published by

关键词： (ntrs) 20130011237: aerial airborne spaceborne computers algorithms bandwidth castano, rebecca chien, steve a. data acquisition downlinking hayden, david image processing imagery nasa technical reports server (ntrs) onboard onboard data processing prioritization satellite observation thompson, david r.

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NASA Technical Reports Server (Ntrs) 20120010448: onboard Science and Applications Algorithm for Hyperspectral data Reduction

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2016年

NASA Technical Reports Server (Ntrs) 20120010448: onboard Science and Applications Algorithm for Hyperspectral data Reduction by NASA Technical Reports Server (Ntrs); NASA Technical Reports Server (Ntrs); published by

关键词： (ntrs) 20120010448: algorithm algorithms applications chien, steve a. data reduction davies, ashley g. hyperspectral mandl, daniel nasa technical reports server (ntrs) onboard data processing remote sensing science sea surface temperature silverman, dorothy spectral bands surface water temperature inversions

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NASA Technical Reports Server (Ntrs) 20030022681: onboard Processor for Compressing Hsi data

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2017年

NASA Technical Reports Server (Ntrs) 20030022681: onboard Processor for Compressing Hsi data by NASA Technical Reports Server (Ntrs); published by

关键词： (ntrs) 20030022681: airborne spaceborne computers algorithms applications programs (computers) architecture (computers) commercial off-the-shelf products compressing cook, sid cost effectiveness data compression data flow analysis day, john h. [technical monitor] harsanyi, joe nasa technical reports server (ntrs) onboard onboard data processing processor spacecraft design spectrum analysis

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NASA Technical Reports Server (Ntrs) 19820008023: Earth Feature Identification for onboard Multispectral data Editing: Computational Experiments

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2016年

NASA Technical Reports Server (Ntrs) 19820008023: Earth Feature Identification for onboard Multispectral data Editing: Computational Experiments by NASA Technical Reports Server (Ntrs); published by

关键词： (ntrs) 19820008023: aherron, r. m. arduini, r. f. computational davis, r. e. determinants earth editing: huck, r. o. mathematical models multispectral photography nasa technical reports server (ntrs) onboard data processing optical data processing park, s. k. radiative transfer remote sensing solar constant spectral reflectance spectral signatures stochastic processes

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Deep-Learning-Based System for Change Detection onboard Earth Observation Small Satellites

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 8115-8124页

作者： Serief, Chahira Ghelamallah, Youcef Bentoutou, Youcef Satell Dev Ctr Oran 31130 Algeria

In recent years, the important evolution in the number, potentiality, and diversity of Earth observation (EO) satellites has resulted in dramatic increases in the payload data volume and rate. However, these exponential increases in the generated data volume are creating a significant bottleneck onboard EO satellites due to transmission bandwidth limits and communication delays. onboard imaging payload data processing can provide an appropriate solution to alleviate the induced data bottleneck. It can also facilitate rapid response for decision-making operations. Change detection is one of the most significant functions in onboard payload data processing systems that enable a real-time reaction to natural disasters, such as flooding, earthquakes, and volcanic eruptions. In this article, we address the problem of automatic change detection onboard EO satellites. This article aims to design an automatic onboard change detection system (OCDS) that can run on existing flight-proven hardware by taking advantage of the attractive features of a leading model in deep learning (DL) called convolutional neural network. The contribution of this article is twofold. An efficient algorithmic solution for change detection based on DL that fulfills space environment-induced constraints is first proposed. Second, a preliminary hardware architecture of the proposed OCDS is designed based on payload data processing flight-proven hardware. The experimental results demonstrate the efficiency of the proposed DL-based change detection approach and the suitability of the designed OCDS for onboarding on EO small satellites.

关键词： Change detection deep learning (DL) Earth observation (EO) satellites onboard data processing optical satellite images

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ELSSIE: A compact stereo spectral imager for planetary surface morphology and composition

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PLANETARY AND SPACE SCIENCE 2024年 241卷

作者： Murchie, Scott L. Seelos, Frank P. Ehlmann, Bethany L. Boldt, John D. Brown, Lawrence E. Greenberg, Jacob M. Hibbitts, Karl A. Lees, W. Jeffrey Linko, David M. Linden, Joseph J. Murphy, Graham P. Nunez, Jorge I. Rorschach, Katherine L. Tinsman, Calley L. Winterling, Frank Johns Hopkins Univ Appl Phys Lab 11101 Johns Hopkins Rd Laurel MD 20723 USA CALTECH 1200 E Calif Blvd Pasadena CA 91125 USA

Here we describe the design, prototyping, testing, and simulations that were conducted to demonstrate the technology for a concept of the next generation landed planetary spectral imager, the Europa Lander Stereo Spectral Imaging Experiment (ELSSIE). The concept was developed originally for a Europa Lander mission, but the design is applicable, with simplifications, to any ocean world of the outer solar system or to non-icy bodies, including Enceladus, the Moon, Mars, or the surface of Ceres. ELSSIE's design consists of two subassemblies. A Sensor melds a high-resolution, 20 -filter, 0.4-3.65 mu m, adjustable-focus multispectral stereo imager with a 0.8-3.6 mu m point spectrometer, sharing a radiation-shielded single Teledyne H2RG 2048 x 2048 pixel focal plane array (FPA). Each camera includes two 6 -position filter wheels with 5 filters and a blank position, providing 10 bandpasses for each of the 2 stereo eyes, and uses 700 x 700 pixels of the FPA. The point spectrometer uses a 6 x350 pixel strip of the FPA. The Sensor provides stereo and imaging/spectroscopic measurements of reflected light from visible to medium wave-infrared (MWIR) wavelengths to characterize surface morphology, search for pyroclastic plumes, search for organics, identify salts and possible biominerals, characterize crystalline vs. amorphous ice and ice grain sizes, and map the distributions of key phases. In addition to addressing important geologic questions, these measurements support selection of a site for in situ sampling and analysis. A data processing Unit (DPU) performs mitigation of radiation that penetrates the shielding using sets of same -filter image frames or spectra of a single spot by removing image spatial pixels with radiation hits, and coadding the remainder for the same spatial pixel, improving signal -to -noise ratio (SNR). The DPU also performs onboard calibration of imager and spectrometer data, co -registration of multispectral images, and calculation of spectral index

关键词： Europa Imagers Spectrometers onboard data processing

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Innovative technologies for space-based radars

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IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION 2003年第3期150卷 104-112页

作者： Zahn, R Astrium GmbH D-88039 Friedrichshafen Germany

In recent years technology developments for SAR front-end and for on-board SAR data processing have been performed at Astrium GmbH, Germany. Remarkable progress has been made in the respective demonstrator projects DE... 详细信息

关键词： synthetic aperture radar radar imaging image coding data compression research initiatives innovative technologies space-based radar SAR front-end on-board SAR data processing Astrium Germany demonstrator projects DESA EUCLID RTP 9.7 TOP Spaceborne radar Synthetic aperture radar image coding radar front-ends data compression onboard data processing Radar imaging

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Removing data Dependencies in the CCSDS 123.0-B-2 Predictor Weight Updating

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Barrios, Yubal Bartrina-Rapesta, Joan Hernandez-Cabronero, Miguel Sanchez, Antonio Blanes, Ian Serra-Sagrista, Joan Sarmiento, Roberto Univ Las Palmas De Gran Canaria ULPGC Inst Appl Microelect IUMA Las Palmas Gran Canaria 35017 Spain Univ Autonoma Barcelona Dept Informat & Commun Engn Bellaterra 08193 Spain

The Consultative Committee for Space data Systems (CCSDS) first standardized near-lossless coding capabilities in the CCSDS 123.0-B-2 algorithm. However, this standard does not describe strategies to produce high-throughput hardware implementations, which are not trivial to derive from its definition. At the same time, throughput optimizations without significant compression performance penalties are paramount to enable real-time compression on-board next-generation satellites. This work demonstrates that the weight update stage of the CCSDS 123.0-B-2 predictor can be selectively bypassed to enhance throughput for both lossless and near-lossless modes with minimal impact on compression performance and still produce fully compliant bitstreams. Skipping the weight update implies that those weights must be carefully chosen outside the original CCSDS 123.0-B-2 pipeline. Two strategies are proposed to select effective weight values based on whether a priori information about the current image is exploited or not. Comprehensive experimental results are presented for both proposed strategies and for lossless and near-lossless regimes, using a representative set of hyperspectral images. The coding penalty is, on average, 1% for lossless and 8% for near-lossless, depending on the strategy used to set the initial weights. The proposed method obtains a maximum throughput of one processed sample per clock cycle when it is evaluated using high-level synthesis (HLS), consuming 4.6% of the look-up tables (LUTs) and 31.1% of the internal memory on a Xilinx Kintex UltraScale space-grade field programmable gate array (FPGA).

关键词： Compression algorithms Consultative Committee for Space data Systems (CCSDSs) 1230-B-2 high throughput hyperspectral imaging onboard data processing

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The Board Certification Process

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FOOT & ANKLE INTERNATIONAL 2019年第7期40卷 866-867页

作者： Pinzur, Michael S. Loyola Univ Hlth Syst Dept Orthopaed Surg & Rehabil Maywood IL 60153 USA

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LSTS Toolchain Framework for Deep Learning Implementation into Autonomous Underwater Vehicle

LSTS Toolchain Framework for Deep Learning Implementation in...

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OCEANS Conference

作者： Aubard, Martin Madureira, Ana Madureira, Luis Campos, Renato Costa, Maria Pinto, Jose Sousa, Joao OceanScan Marine Syst & Technol Matosinhos Portugal Interdisciplinary Studies Res Ctr ISRC ISEP IPP Porto Portugal UP Lab Sistemas & Tecnol Subaquat FE Porto Portugal

ISBN: (纸本)9798350332261

The development of increasingly autonomous underwater vehicles has long been a research focus in underwater robotics. Recent advances in deep learning have shown promising results, offering the potential for fully autonomous behavior in underwater vehicles. However, its implementation requires improvements to the current vehicles. This paper proposes an onboard data processing framework for Deep Learning implementation. The proposed framework aims to increase the autonomy of the vehicles by allowing them to interact with their environment in real time, enabling real-time detection, control, and navigation.

关键词： Autonomous Underwater Vehicles Deep Learning onboard data processing Object Detection onboard Tracking

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