Volume XXXIX-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 217-222, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B7-217-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 217-222, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B7-217-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 Jul 2012

31 Jul 2012

RESEARCH ON DIFFERENTIAL CODING METHOD FOR SATELLITE REMOTE SENSING DATA COMPRESSION

Z. J. Lin1, N. Yao2, B. Deng3, C. Z. Wang4, and J. H. Wang4 Z. J. Lin et al.
  • 1Chinese Academy of Surveying and Mapping, 16 Beitaiping Road, Beijing, China
  • 2Remote Sensing information Engineering School, Wuhan University, 129 Luoyu Road, Wuhan, China
  • 3Beijing Ceke Spatial Information Technology Company, Ltd., 16 Beitaiping Road, Beijing, China
  • 4Guizhou Guihang Unmanned Aerial Vehicles Company, Ltd., 87 West City Road, AnShun, China

Keywords: Satellite, Compression, Land cover, Differential encoding, Information amount, Compression rate

Abstract. Data compression, in the process of Satellite Earth data transmission, is of great concern to improve the efficiency of data transmission. Information amounts inherent to remote sensing images provide a foundation for data compression in terms of information theory. In particular, distinct degrees of uncertainty inherent to distinct land covers result in the different information amounts. This paper first proposes a lossless differential encoding method to improve compression rates. Then a district forecast differential encoding method is proposed to further improve the compression rates. Considering the stereo measurements in modern photogrammetry are basically accomplished by means of automatic stereo image matching, an edge protection operator is finally utilized to appropriately filter out high frequency noises which could help magnify the signals and further improve the compression rates. The three steps were applied to a Landsat TM multispectral image and a set of SPOT-5 panchromatic images of four typical land cover types (i.e., urban areas, farm lands, mountain areas and water bodies). Results revealed that the average code lengths obtained by the differential encoding method, compared with Huffman encoding, were more close to the information amounts inherent to remote sensing images. And the compression rates were improved to some extent. Furthermore, the compression rates of the four land cover images obtained by the district forecast differential encoding method were nearly doubled. As for the images with the edge features preserved, the compression rates are average four times as large as those of the original images.