Hyperspectral Image Compression Using Inter-band Prediction

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Implementation of hyperspectral image compression through inter-band prediction techniques, including unidirectional and bidirectional prediction methods with optimal reference band selection strategies

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In this article, we explore hyperspectral image compression using inter-band prediction techniques. We will examine both unidirectional prediction, where subsequent bands are predicted from preceding reference bands, and bidirectional prediction, which leverages both previous and subsequent bands for more accurate compression. The implementation typically involves calculating correlation coefficients between bands to identify optimal reference bands, followed by applying prediction algorithms like differential pulse-code modulation (DPCM). Key functions would include band correlation analysis, prediction error calculation, and entropy coding optimization. We will also discuss methodologies for selecting optimal reference bands, which may involve algorithms that minimize prediction error across spectral dimensions. These techniques significantly improve compression efficiency while maintaining image quality and spectral accuracy in hyperspectral data processing.

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