Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Although hyperspectral imagery (HSI), which has been applied in a wide range of applications, suffers from very large volumes of data, its uncompressed representation is still preferred to avoid compression loss for accurate data analysis. In this paper, we focus on quality-assured lossy compression of HSI, where the accuracy of analysis from decoded data is taken as a key criterion to assess the efficacy of coding. An improved 3D discrete cosine transform-based approach is proposed, where a support vector machine (SVM) is applied to optimally determine the weighting of inter-band correlation within the quantization matrix. In addition to the conventional quantitative metrics signal-to-noise ratio and structural similarity for performance assessment, the classification accuracy on decoded data from the SVM is adopted for quality-assured evaluation, where the set partitioning in hierarchical trees (SPIHT) method with 3D discrete wavelet transform is used for benchmarking. Results on four publically available HSI data sets have indicated that our approach outperforms SPIHT in both subjective (qualitative) and objective (quantitative) assessments for land-cover analysis in remote-sensing applications. Moreover, our approach is more efficient and generates much reduced degradation for subsequent data classification, hence providing a more efficient and quality-assured solution in effective compression of HSI.