NDLI: An Operational Approach to PCA+JPEG2000 Compression of Hyperspectral Imagery

Content Provider	IEEE Xplore Digital Library
Author	Qian Du Nam Ly Fowler, J.E.
Copyright Year	2008
Abstract	Lossy-compression algorithms typically adopted for hyperspectral remote-sensing imagery-such as JPEG2000-usually produce a monotonically increasing signal-to-noise ratio (SNR) for increasing bitrate. Consequently, it is a common philosophy to employ as large a bitrate as possible so as to obtain the highest achievable SNR. However, it has been observed previously that a higher SNR may not necessarily correspond to better performance at data-analysis tasks, such as classification, anomaly detection, or linear unmixing. Considered specifically is the coupling of JPEG2000 with principal component analysis for spectral decorrelation such that only a few principal components are retained, and, for this compression paradigm, a technique to determine an operational bitrate is proposed with the aim of preserving both the majority of information in a dataset as well as its anomalous pixels. This operational bitrate may be much less than the largest bitrate that the system can allow. Experimental results show that classification and unmixing applied to reconstructed data after compression at this operational bitrate result in performance that is the same as or better than that achieved at higher bitrates; meanwhile, removal and lossless storage of anomalies prior to compression results in their perfect preservation in the reconstructed dataset.
Starting Page	2237
Ending Page	2245
Page Count	9
File Size	2237368
File Format	PDF
ISSN	19391404
Volume Number	7
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bit rate Transform coding Signal to noise ratio Image coding Principal component analysis Hyperspectral imaging Accuracy linear unmixing Hyperspectral image compression classification anomaly detection
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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