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Open Access research with a European policy impact...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the European Policies Research Centre (EPRC).

EPRC is a leading institute in Europe for comparative research on public policy, with a particular focus on regional development policies. Spanning 30 European countries, EPRC research programmes have a strong emphasis on applied research and knowledge exchange, including the provision of policy advice to EU institutions and national and sub-national government authorities throughout Europe.

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

Qiao, Tong and Ren, Jinchang and Sun, Meijun and Zheng, Jiangbin and Marshall, Stephen (2014) Effective compression of hyperspectral imagery using an improved 3D DCT approach for land cover analysis in remote sensing applications. International Journal of Remote Sensing, 35 (20). pp. 7316-7337. ISSN 0143-1161

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Abstract

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 (DCT) based approach is proposed, where a Support Vector Machine (SVM) is applied to optimally determine the weighting of inter-band correlation within the quantisation matrix. In addition to the conventional quantitative metrics Signal-to-Noise Ratio (SNR) and Structural Similarity (SSIM) 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 (DWT) is used for benchmarking. Results on four publically available HSI datasets 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 provides a more efficient and quality-assured solution in effective compression of HSI.