XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/4: METHODS FOR LAND COVER CLASSIFICATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: RESEARCH ON DIFFERENTIAL CODING METHOD FOR SATELLITE REMOTE SENSING DATA COMPRESSION Z. J. Lin, N. Yao, B. Deng, C. Z. Wang, J. H. Wang

Document type:: Multivolume work

Structure type:: Chapter

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

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