International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
     
  
RESEARCH ON DIFFERENTIAL CODING METHOD 
FOR SATELLITE REMOTE SENSING DATA COMPRESSION 
Z. J. Lin**, N. Yao®, B. Deng", C. Z. Wang *, J. H. Wang? 
* Chinese Academy of Surveying and Mapping, 16 Beitaiping Road, Beijing, China - lincasm@casm.ac.cn 
® Remote Sensing information Engineering School, Wuhan University, 129 Luoyu Road, Wuhan, China 
nayao@foxmail.com 
* Beijing Ceke Spatial Information Technology Company, Ltd., 16 Beitaiping Road, Beijing, China 
joysdeng@sina.com 
? Guizhou Guihang Unmanned Aerial Vehicles Company, Ltd., 87 West City Road, AnShun, China 
gzwrj@vip.163.com 
KEY WORDS: 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. 
1. INTRODUCTION 
Satellite Earth data transmission is an indispensible link in the 
process of satellite remote sensing. Under many circumstances, 
data compression technology should be considered to improve 
the efficiency of this link. For example, a certain remote 
sensing satellite with a designed data acquisition rate of 840 
Mbps (millions of bits per second/ megabytes per second), 
given a data transmission rate of 450Mbps, calls for an at least 
1.9 times data compression rate so as to ensure a real-time data 
transmission from satellite to Earth. Hence, if a country has not 
yet possessed sufficient capacity of evenly building necessary 
ground receiving stations, remote sensing images should be 
stored by the on-board computers and not be transmitted until 
the satellites fly above the receiving station. As for the 
aforementioned satellite, the on-board stored data obtained from 
above North and South America will be approximately four 
times compressed when it is transmitted to the receiving 
stations in China. 
Different from video camera images, remotely sensed imagery 
is characterized by its lower correlation, higher entropy and 
lower redundancy. Besides, the information contained in remote 
sensing images, e.g., hues and textures, is in abundance. The 
information contained in the on-board stored images is expected 
to be preserved as complete as possible for following image 
interpretations and measurements. Hence, the most favorable 
  
* Corresponding author. 
data compression method for remote sensing imagery is lossless 
compression, or at least near lossless compression which would 
not affect the accuracy of stereo measurements. 
In order to ensure the quality of the acquired remote sensing 
images with sufficiently and effectively usage of the limited 
storage space on-board, compression methods with higher 
compression rates are demanded, the essence of which is to 
reduce the redundancy of images as large as possible. 
2. DIFFERENTIAL CODING FOR LOSSLESS 
COMPRESSION 
2.1 Basis of Information Theory 
Lossless compression refers to a class of data compression 
algorithms that allows the exact original data to be 
reconstructed from the compressed data. The most 
representative lossless compression algorithms include Shannon 
coding, Huffman coding, run-length encoding, Lempel-Ziv- 
Welch (LZW), arithmetic coding and so on. From the 
perspective of information theory, the principle of lossless 
compression is to eliminate the data redundancy. The 
redundancy of images results from two aspects: (1) unevenly 
distributed radiations which may be reflected by grayscale 
histograms; (2) the auto-correlation consisting in time, spatial