A Novel Framework for Object-Based Coding and Compression of Hyperspectral Imagery

ZHAO Chunhui; LI Xiaohui; REN Jinchang; Stephen Marshall

doi:10.1049/cje.2015.04.012

Volume 24 Issue 2

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ZHAO Chunhui, LI Xiaohui, REN Jinchang, et al., “A Novel Framework for Object-Based Coding and Compression of Hyperspectral Imagery,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 300-305, 2015, doi: 10.1049/cje.2015.04.012

Citation:

ZHAO Chunhui, LI Xiaohui, REN Jinchang, et al., “A Novel Framework for Object-Based Coding and Compression of Hyperspectral Imagery,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 300-305, 2015, doi: 10.1049/cje.2015.04.012

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A Novel Framework for Object-Based Coding and Compression of Hyperspectral Imagery

doi: 10.1049/cje.2015.04.012

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2.
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, United Kingdom

Funds: This work is supported by the National Natural Science Foundation of China (No.61077079, No.61275010), the Key Program of Heilongjiang Natural Science Foundation (No.ZD201216), Program Excellent Academic Leaders of Harbin (No.RC2013XK009003) and the Fundamental Research Funds for the Central Universities (No.HEUCF1408).

Publish Date: 2015-04-10

Abstract

Abstract

A novel object-based framework is proposed for HSI compression, where targets of interest are extracted and separately coded. With objects removed, the holes are filled with the background average to form a new but more homogenous background for better compression. An improved sparse representation with adaptive spatial support is proposed for target detection. By applying the proposed framework to 2D/3D DCT approaches, reconstructed images from conventional and proposed approaches are compared. Six criteria in three groups are employed for quantitative evaluations to measure the degree of data reduction, the distortion of reconstructed image quality and accuracy in target detection, respectively. Comprehensive experiments on two datasets are used for performance evaluation. It is found that the proposed approaches yield much improved results.
- Hyperspectral imagery (HSI),
- Objectbased image compression,
- Sparse representation,
- Target detection,
- Remote sensing

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References(15)

References

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