Wavelet-based compression method for scale-preserving in VNIR and SWIR hyperspectral data.

Purpose: Hyperspectral imaging (HSI) collects detailed spectral information across hundreds of narrow bands, providing valuable datasets for applications such as medical diagnostics. However, the large size of HSI datasets, often exceeding several gigabytes, creates significant challenges in data transmission, storage, and processing. We aim to develop a wavelet-based compression method that addresses these challenges while preserving the integrity and quality of spectral information. Approach: The proposed method applies wavelet transforms to the spectral dimension of hyperspectral data in three steps: (1) wavelet transformation for dimensionality reduction, (2) spectral cropping to eliminate low-intensity bands, and (3) scale matching to maintain accurate wavelength mapping. Daubechies wavelets were used to achieve up to 32× compression while ensuring spectral fidelity and spatial feature retention. Results: The wavelet-based method achieved up to 32× compression, corresponding to a 96.88% reduction in data size without significant loss of important data. Unlike principal component analysis and independent component analysis, the method preserved the original wavelength scale, enabling straightforward spectral interpretation. In addition, the compressed data exhibited minimal loss in spatial features, with improvements in contrast and noise reduction compared with spectral binning. Conclusions: We demonstrate that wavelet-based compression is an effective solution for managing large HSI datasets in medical imaging. The method preserves critical spectral and spatial information and therefore facilitates efficient data storage and processing, providing a way for the practical integration of HSI technology in clinical applications.