| Image compression based on wavelet transform has attracted many attentions in recent years. With the development of multispectral remote-sensing technology, real- time transmission on limited bandwidth and storing of multispectral image data has become a challenging problem, so compression of multispectral images is of more and more importance. In this paper, theories about wavelet analysis and image compression are reviewed and researches on air-borne multispectral remote-sensing image compression by wavelet transform are discussed. At first, theories about wavelet analysis are systematically reviewed from the viewpoint of signal analysis and digital signal processing. Then Mallat algorithm is used to realize 2-D wavelet transform and details of the algorithm, especially selection criterion of wavelet basis (filter banks), are discussed according to the requirements of image compression. Thirdly, existing image encoding technologies by wavelet transform are reviewed based on the analysis of wavelet transform coefficients characters. SPIHT (Set Partitioning in Hierarchical Trees) encoding algorithm is realized and its performance is testified to be superior to that of JPEG. In the end, SPIHT is used to realize the interband compression of multispectral images and two methods, wavelet transform and one-order predictor, are used to spectrally decorrelate the multispectral data. In order to enhance the performance of predictor, a subgroup DPCM algorithm is proposed. Experiment results show that this subgroup DPCM+SPIHT algorithm is efficient. The nearlossless compression ratio is above 6 for the data used in this paper and details in images are kept well when compression ratio is above 20, while encoding time for all 64 multispectral images is not more than 90 seconds. The algorithm is also easy to implement by hardware, so it is a practicable solution to air-borne multispectral remote-sensing image compression.
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