A Simple Lossless Algorithm for on-board Satellite Hyperspectral Data Compression

As resolution of on-board imaging spectrometer keeps improving, data acquisition rate increases and resource limited satellite environment necessitates for computationally simple data compression methods to meet timing, bandwidth and resource requirements with error resilience. This letter proposes...

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Veröffentlicht in:IEEE geoscience and remote sensing letters Jg. 20; S. 1
Hauptverfasser: Joshi, Vijay, Sheeba Rani, J
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive sampling
Algorithms
Complexity
Compression
Computational complexity
Computer applications
Data acquisition
Data compression
Data processing
Data systems
Decision making
Hyperspectral data compression
Hyperspectral imaging
Image coding
Imaging
Imaging spectrometers
Lossless compression
Methods
Minimization
Neighbor driven decision making
Non-binary tree traversal
Onboard
Performance evaluation
Predictions
Satellite data compression
Satellites
Table lookup
ISSN:1545-598X, 1558-0571
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Zusammenfassung:As resolution of on-board imaging spectrometer keeps improving, data acquisition rate increases and resource limited satellite environment necessitates for computationally simple data compression methods to meet timing, bandwidth and resource requirements with error resilience. This letter proposes a new lossless, prediction based algorithm for on-board satellite hyperspectral data compression that utilizes spectral as well as spatial correlation and at the same time, is computationally less complex. Concept of non-binary tree traversal is used with nearest neighbor method and implemented using neighbor driven decision making in pre-processing stage. Previously processed pixels are used to minimize the prediction residual, which makes more than 80% calculations causal in nature and thereby reducing the computational complexity of the algorithm. The prediction residual is then encoded using sample adaptive Golomb coding in band-sequential order. CCSDS corpus of data for hyperspectral images is used for evaluating the performance of the algorithm. The proposed method shows reduced computational complexity and lesser data dependencies compared to the CCSDS 123.0-B-1 standard when similar spectral vicinity is considered, and comparable compression performance compared to other state-of-the-art on-board lossless compression methods.
Bibliographie:ObjectType-Article-1
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2023.3275436