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检索条件"任意字段=Conference on Satellite Data Compression, Communications, and Processing VI"

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satellite data compression, communications, and processing vi

Satellite Data Compression, Communications, and Processing V...

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satellite data compression, communications, and processing vi

ISBN: (纸本)9780819483065

The proceedings contain 38 papers. The topics discussed include: rate allocation method for the fast transmission of pre-encoded meteorological data over JPIP;hyperspectral compressive sensing;hyperspectral image compression algorithm using wavelet transform and independent component analysis;robust video multicast scheme over wireless network with priority transmission;watermarking scheme for tampering detection in remote sensing images using variable size tiling and DWT;comparison of support vector machine-based processing chains for hyperspectral image classification;fast multi-spectral image registration based on a statistical learning technique;high-order statistics Harsanyi-Farrand-Chang method for estimation of virtual dimensionality;remote sensing image restoration based on compressive sensing and two-step iteration shrinkage algorithm;and enhancement of spatial resolution of hyperspectral imagery using iterative back projection.

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Implementation of CCSDS data compression for remote sensing image

Implementation of CCSDS data compression for remote sensing ...

引用

conference on satellite data compression, communications, and processing vi

作者： Lin, Albert Chang, C. F. Lin, M. C. Jan, L. J. Ren, Hsuan National Space Organization (Taiwan) Chung-Shan Institute of Science and Technology (Taiwan) National Central Univ. (Taiwan)

ISBN: (纸本)9780819483065

The FORMOSAT-5 is an optical remote sensing satellite with PAN 2m and MS 4m resolution, which is under development by the National Space Organization (NSPO) in Taiwan. The payload consists of one Panchromatic (PAN) band with 12,000 pixels and four Multi-Spectrum (MS) bands with 6000 pixels in the remote sensing instrument. The image data compression method complies with the Consultative Committee for Space data Systems (CCSDS) standards(1,2). The compression ratio can be 1.5 for lossless compression, 3.75 or 7.5 for lossy compression. The Xilinx virtex-5Q FPGA, XQR5VFX130 is used to achieve near real time compression.

关键词： FORMOSAT-5 CCSDS Image data compression FPGA

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A GPU-Based Implementation of Predictive Partitioned Vector Quantization for compression of Ultraspectral Sounder data

A GPU-Based Implementation of Predictive Partitioned Vector ...

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conference on satellite data compression, communications, and processing vi

作者： Wei, Shih-Chieh Huang, Bormin Tamkang Univ Dept Informat Management Tamsui 25137 Taiwan Univ Wisconsin CIMSS SSEC Madison WI 53706 USA

ISBN: (纸本)9780819483065

Recently there is a boom on the use of graphic processor units (GPU) for speedup of scientific computations. By identifying the time dominant portions of the code that can be executed in parallel, significant speedup can be achieved by a GPU-based implementation. For the voluminous ultraspectral sounder data, lossless compression is desirable to save storage space and transmission time without losing precision in retrieval of geophysical parameters. Predictive partitioned vector quantization (PPVQ) has been proven to be an effective lossless compression scheme for ultraspectral sounder data. It consists of linear prediction, bit partition, vector quantization, and entropy coding. Two most time consuming stages of linear prediction and vector quantization are chosen for GPU-based implementation. By exploiting the data parallel characteristics of these two stages, a speedup of 42x has been achieved in our GPU-based implementation of the PPVQ compression scheme.

关键词： graphic processor unit predictive partitioned vector quantization lossless data compression

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A New System to Perform Unsupervised and Supervised Classification of satellite Images from Google Maps

A New System to Perform Unsupervised and Supervised Classifi...

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conference on satellite data compression, communications, and processing vi

作者： Bernabe, Sergio Plaza, Antonio Univ Extremadura Hyperspectral Comp Lab Dept Technol Comp & Commun E-10071 Caceres Spain

ISBN: (纸本)9780819483065

In this paper, we describe a new system for unsupervised and supervised classification of satellite images from Google Maps. The system has been developed using the SwingX-WS library, and incorporates functionalities such as unsupervised classification of image portions selected by the user (at the maximum zoom level) using ISOdata and k-Means, and supervised classification using the Minimum Distance and Maximum Likelihood, followed by spatial post-processing based on majority voting. Selected regions in the classified portion are used to train a maximum likelihood classifier able to map larger image areas in a manner transparent to the user. The system also retrieves areas containing regions similar to those already classified. An experimental validation of the proposed system has been conducted by comparing the obtained classification results with those provided by commercial software, such as the popular Research Systems ENvi package.

关键词： satellite image classification Google Maps

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Weighted-based Arithmetic Coding For satellite Image compression

Weighted-based Arithmetic Coding For Satellite Image Compres...

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conference on satellite data compression, communications, and processing vi

作者： Wu, Jiaji Gao, Shuai Jiao, Licheng Jeong, Jechang Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Hanyang Univ Dept Elect & Comp Engn Coll Engn Seoul 133791 South Korea

ISBN: (纸本)9780819483065

Aiming at the problems of the context-dilution and complicated context-quantization of high-order context, this paper proposes a new context arithmetic coding using weighted-based context modeling. By classifying the weights with non-uniform quantization, conventional high-order context-based arithmetic coding method can be approximated as low-order arithmetic coding. Compared with the existing high-order context modeling, the proposed method not only decreases the complexity of computation but also effectively improves the performance of entropy coding. Experimental results show that the algorithm with proposed weighted-based arithmetic coding method performs better than SPECK, SPIHT and JPEG2000.

关键词： arithmetic coding context-based high-order context context-dilution weighted-based classification

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Simple resiliency improvement of the CCSDS standard for lossless data compression

Simple resiliency improvement of the CCSDS standard for loss...

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conference on satellite data compression, communications, and processing vi

作者： Clotet, Marcial Portell, Jordi villafranca, Alberto G. Garcia-Berro, Enrique Univ Politecn Cataluna Dept Fis Aplicada C Esteve Terrades 5 Castelldefels 08860 Spain Inst Estudis Espacials Catalunya Barcelona 08034 Spain Univ Barcelona Dept Astron & Meteorol E-08028 Barcelona Spain

ISBN: (纸本)9780819483065

The Consultative Committee for Space data Systems (CCSDS) recommends the use of a two-stage strategy for lossless data compression in space. At the core of the second stage is the Rice coding method. The Rice compression ratio rapidly decreases in the presence of noise and outliers, since this coder is specially conceived for noiseless data following geometric distributions. This, in turn, makes the CCSDS recommendation too sensitive in front of outliers in the data, leading to non-optimal ratios in realistic scenarios. In this paper we propose to substitute the Rice coder of the CCSDS recommendation by a subexponential coder. We show that this solution offers high compression ratios even when large amounts of noise are present in the data. This is done by testing both compressors with synthetic and real data. The performance is actually similar to that obtained with the FAPEC coder, although with slightly higher processing requirements. Therefore, this solution appears as a simple improvement that can be done to the current CCSDS standard with an excellent return.

关键词： data compression lossless CCSDS resilient robust Rice subexponential

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Optimizing GPS data transmission using entropy coding compression

Optimizing GPS data transmission using entropy coding compre...

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conference on satellite data compression, communications, and processing vi

作者： villafranca, Alberto G. Mora, Iu Ruiz-Rodriguez, Patricia Portell, Jordi Garcia-Berro, Enrique Univ Politecn Cataluna Dept Fis Aplicada C Esteve Terrades 5 Castelldefels 08860 Spain Inst Estudis Espacials Catalunya Barcelona 08034 Spain Univ Barcelona Dept Astron & Meteorol E-08028 Barcelona Spain

ISBN: (纸本)9780819483065

The Global Positioning System (GPS) has long been used as a scientific tool, and it has turned into a very powerful technique in domains like geophysics, where it is commonly used to study the dynamics of a large variety of systems, like glaciers, tectonic plates and others. In these cases, the large distances between receivers as well as their remote locations usually pose a challenge for data transmission. The standard format for scientific applications is a compressed RINEX file - a raw data format which allows post-processing. Its associated compression algorithm is based on a pre-processing stage followed by a commercial data compressor. In this paper we present a new compression method which can achieve better compression ratios with a faster operation. We have improved the pre-compression stage, split the resulting file into two, and applied the most appropriate compressor to each file. FAPEC, a highly resilient entropy coder, is applied to the observables file. The results obtained so far demonstrate that it is possible to obtain average compression gains of about 35% with respect to the original compressor.

关键词： data compression GPS RINEX FAPEC PEC Hatanaka

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Overview of NOAA's hyper-spectral sounding data, communication, and compression activities

Overview of NOAA's hyper-spectral sounding data, communicati...

引用

conference on Atmospheric and Environmental Remote Sensing data processing and Utilization

作者： Heymann, RW Schmith, TJ NOAA Natl Environm Satellite Data & Informat Serv Off Syst Dev Silver Spring MD 20910 USA

ISBN: (纸本)0819454869

Instruments on the next generation operational GOES will generate an expected 50 times greater amount of data than their predecessors on today's GOES. Part of this increase is due to the high-spectral resolution data from the Hyperspectral Environmental Suite (HES). The data increase led to the need to research the impact to the GOES satellite's end-to-end communication system, and research compression of hyper-spectral sounder sensor data. The compression research, in mathematics includes, but is not limited to, wavelets, principal components, adaptive clustering, empirical mode decomposition. The high data rates require additional RF spectrum, in the congested 8025-8400 MRZ band X-band. Congestion in the geo orbit X and L-bands mandate extensive efforts to control out-of-band RF emissions. The need for communication signal data quality with few errors (low BER) is essential for NOAA science use. Limited bandwidth and the need to be efficient in bandwidth use, force the consideration of bandwidth efficient modulations. These communication issues force the use of the latest technologies. The research has shown that GOES will be able to downlink all of the future sensor data, and could continue to be used as a communication satellite to distribute large volumes of its sensor information to U.S. and foreign users.

关键词： GOES communications hyperspectral sounder compression satellite spectrum

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Rate allocation method for the fast transmission of pre-encoded meteorological data over JPIP

Rate allocation method for the fast transmission of pre-enco...

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conference on satellite data compression, communications, and processing vi

作者： Jimenez-Rodriguez, Leandro Auli-Llinas, Francesc Munoz-Gomez, Juan Bartrina-Rapesta, Joan Blanes, Ian Serra-Sagrista, Joan Autonomous Univ Barcelona Dept Informat & Commun Engn E-08193 Cerdanyola Del Valles Spain

ISBN: (纸本)9780819483065

This work addresses the transmission of pre-encoded video containing meteorological data over JPIP. The primary requirement for the rate allocation algorithm deployed in the JPIP server is the real-time processing demands of the application. A secondary requirement for the proposed algorithm is that it should be able to either minimize the mean squared error (MMSE) of the video sequence, or minimize the maximum distortion (MMAX). The MMSE criterion considers the minimization of the overall distortion, whereas MMAX achieves pseudo-constant quality for all frames. The proposed rate allocation method employs the FAst rate allocation through STeepest descent (FAST) method that was initially developed for video-on-demand applications. The adaptation of FAST in the proposed remote sensing scenario considers meteorological data captured by the European meteorological satellites (Meteosat). Experimental results suggest that FAST can be successfully adopted in remote sensing scenarios.

关键词： Rate allocation rate-distortion optimization interactive image transmission

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CNES Studies for On-Board implementation via HLS tools of a Cloud-Detection module for Selective compression

CNES Studies for On-Board implementation via HLS tools of a ...

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conference on satellite data compression, communications, and processing vi

作者： Camarero, R. Thiebaut, C. Dejean, Ph Speciel, A. CNES Paris France MAGELLIUM Ramonville St Agne France DELTA TECHNOL SUD OUEST Toulouse France

ISBN: (纸本)9780819483065

Future CNES high resolution instruments for remote sensing missions will lead to higher data-rates because of the increase in resolution and dynamic range. For example, the ground resolution improvement has induced a data-rate multiplied by 8 from SPOT4 to SPOT5 [1] and by 28 to PLEIADES-HR [2]. Innovative "smart" compression techniques will be then required, performing different types of compression inside a scene, in order to reach higher global compression ratios while complying with image quality requirements. This so-called "selective compression", allows important compression gains by detecting and then differently compressing the regions-of-interest (ROI) and non-interest in the image (e. g. higher compression ratios are assigned to the non-interesting data). Given that most of CNES high resolution images are cloudy [1], significant mass-memory and transmission gain could be reached by just detecting and suppressing (or compressing significantly) the areas covered by clouds. Since 2007, CNES works on a cloud detection module [3] as a simplification for on-board implementation of an already existing module used on-ground for PLEIADES-HR album images [4]. The different steps of this Support Vector Machine classifier have already been analyzed, for simplification and optimization, during this on-board implementation study: reflectance computation, characteristics vector computation (based on multispectral criteria) and computation of the SVM output. In order to speed up the hardware design phase, a new approach based on HLS [5] tools is being tested for the VHDL description stage. The aim is to obtain a bit-true VDHL design directly from a high level description language as C or Matlab/Simulink [6].

关键词： selective compression satellite images cloud-detection high level synthesis

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