Research Papers

Subsource-based compression in remote sensing

[+] Author Affiliations
Tao Li, Xin Tian, Yan-Sheng Li, Shui-Ping Zhang, Jin-Wen Tian

Huazhong University of Science and Technology, National Key Laboratory of Science & Technology on Multi-Spectral Information Processing, 1037 Luoyu Road, Wuhan 430074, China

Cheng-Yi Xiong

South-Central University for Nationalities, College of Electronic and Information Engineering, Hubei Key Laboratory of Intelligent Wireless Communication, 182 Minyuan Road, Hongshan District, Wuhan 430074, China

J. Appl. Remote Sens. 7(1), 073555 (May 29, 2013). doi:10.1117/1.JRS.7.073555
History: Received July 31, 2012; Revised March 25, 2013; Accepted April 30, 2013
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Abstract.  Classical compression methods of remote sensing (RS) panchromatic images are much the same as the traditional compression ones, in which distributions of different surface features are not taken into account. Instead, RS panchromatic images are divided into blocks in our method and those blocks can be classified into several categories by analyzing their intensity distributions. Afterwards, each category is compressed separately. According to Shannon’s theorem 3, a source with given distribution and distortion has a unique theoretical minimum bitrate. Hence, under a given compression quality, the theoretical minimum bitrate of each category can be calculated using rate-distortion theory. Meanwhile, each category may have its own distortion due to the user’s different quality requirements. Our method performs well in reducing the redundancy of surface features which users do not care about so that more “valid data” would be obtained from the compressed images. Furthermore, it also provides flexibility between fixed compression ratio and quality-based compression.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Tao Li ; Xin Tian ; Cheng-Yi Xiong ; Yan-Sheng Li ; Shui-Ping Zhang, et al.
"Subsource-based compression in remote sensing", J. Appl. Remote Sens. 7(1), 073555 (May 29, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.073555


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