A Comparative Analysis of Adaptive Encoding Techniques for Multispectral Images

Abstract

Multispectral images are images with high spatial, spectral and radiometric resolution. Efficient multispectral image compression plays a key role in most of the geographical applications. The three important phases involved in a adaptive technique are transformation, clustering and encoding based on the resultant clusters. The existing methods like SPIHT, STW, WDR, and ASWDR are used as the base for Adaptive encoding techniques. The methods adopted are the recent wavelet compression techniques adopted for high dimensional images. The paper proposes the adaptive encoding technique and different combination of encoding techniques are evaluated and an efficient technique (E –Ad) is identified by comparing the results using the metrics Peak Signal to Noise Ratio as well as Compression Ratio. The proposed compression technique preserves the unique spectral characteristics of the multispectral image. The compressed image, can be efficiently used as preprocessing for many enhancement techniques and for object classification mechanisms. The E-Ad technique overtakes many of the state of art algorithms.