An effective and lossy compression technique for multispectral and hyperspectral image data minimizes both the spatial and spectral correlations while preserving the spectral characteristics of the data. In this paper, we combine wavelet transform and a variant of vector quantization for decorrelating both spatial and spectral information, and thus aim to achieve superior quality. We use 2-D wavelet transform followed by Kronecker-Product Gain-Shape Vector Quantization. This is coupled with the generalized BFOS for obtaining an optimal bit-rate. The pixels within the subbands of multi- and hyper-spectral images exhibit greater spectral redundancy which is thus exploited by designing multiresolution codebooks. Results are presented for multispectral and hyperspectral data taken from different sensors in different bands. The results obtained with our scheme are compared with other techniques designed for multi-/hyper-spectral image data and the Wavelet-based JPEG-2000. The computational requirement of the proposed technique is lower in comparison with other vector-quantization techniques.