Subsource-based compression in remote sensing

Tao Li; Xin Tian; Cheng-Yi Xiong; Yan-Sheng Li; Shui-Ping Zhang; Jin-Wen Tian

doi:10.1117/1.JRS.7.073555

29 May 2013 Subsource-based compression in remote sensing

Tao Li, Xin Tian, Cheng-Yi Xiong, Yan-Sheng Li, Shui-Ping Zhang, Jin-Wen Tian

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 7, Issue 1, 073555 (May 2013). https://doi.org/10.1117/1.JRS.7.073555

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.

Citation Download Citation

Tao Li, Xin Tian, Cheng-Yi Xiong, Yan-Sheng Li, Shui-Ping Zhang, and Jin-Wen Tian "Subsource-based compression in remote sensing," Journal of Applied Remote Sensing 7(1), 073555 (29 May 2013). https://doi.org/10.1117/1.JRS.7.073555

Published: 29 May 2013

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