Satellite imageries which comprises of various multispectral spectral bands pertaining to spectral and spatial information of the images acquired by latest multispectral sensor technology are rapidly increasing day by day in the recent years for onboard satellite remote sensing applications. A lossy multispectral image compression is desired by the exploitation of the redundancies present in the spatial and spectral information while preserving the vital and crucial information of the image objects to a certain extent. In this paper a novel approach is proposed for lossy multispectral image compression which is an extension to the earlier existing algorithms. In this proposed method the multispectral images are first enhanced with interpolation based super resolution technique to estimate a hi-resolution (HR) image from a low-resolution (LR) input image. Secondly the decorrelated spectral bands transformed by discrete wavelet transform (DWT), which contain maximum entropy, are selected and these representative spectral bands are quantized and encoded using Improved SPIHT (ISPIHT) algorithm. The algorithm has been designed for the optimization of maximum coding efficiency and for high compression ratio of bits per pixel per band when compared with the well known compression techniques.

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