| タイトル | Hardware Implementation of Lossless Adaptive and Scalable Hyperspectral Data Compression for Space |
| 著者（英） | Klimesh, Matthew; Bakhshi, Alireza; Aranki, Nazeeh; Keymeulen, Didier |
| 著者所属（英） | Jet Propulsion Lab., California Inst. of Tech. |
| 発行日 | 2009-07-29 |
| 言語 | eng |
| 内容記述 | On-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. The technique also improves signature extraction, object recognition and feature classification capabilities by providing exact reconstructed data on constrained downlink resources. At JPL a novel, adaptive and predictive technique for lossless compression of hyperspectral data was recently developed. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that far exceeds state-of-the-art techniques currently in use. The JPL-developed 'Fast Lossless' algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. It is of low computational complexity and thus well-suited for implementation in hardware. A modified form of the algorithm that is better suited for data from pushbroom instruments is generally appropriate for flight implementation. A scalable field programmable gate array (FPGA) hardware implementation was developed. The FPGA implementation achieves a throughput performance of 58 Msamples/sec, which can be increased to over 100 Msamples/sec in a parallel implementation that uses twice the hardware resources This paper describes the hardware implementation of the 'Modified Fast Lossless' compression algorithm on an FPGA. The FPGA implementation targets the current state-of-the-art FPGAs (Xilinx Virtex IV and V families) and compresses one sample every clock cycle to provide a fast and practical real-time solution for space applications. |
| NASA分類 | Computer Operations and Hardware; Electronics and Electrical Engineering |
| 権利 | Copyright |
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