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

Type of content:: Konferenzschrift

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Reihe:: ISPRS archives

Language:: English

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

Editor:: International Society for Photogrammetry and Remote Sensing

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

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Type of content:: Konferenzschrift

DOI:: 10.14463/KXP:1663821976

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Reihe:: ISPRS archives (volume 39, B7 (2012))

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)

Editor:: International Society for Photogrammetry and Remote Sensing

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

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

In Table 4, as the smoothing times increase, the entropies and information amounts are correspondingly reduced, which reveals information loss during the edge protection smoothing process. Meanwhile, the gradually blurred images (in Figure 6) after increased times of smoothing also suggest the information loss to some extent. Therefore, the edge preserved smoothing method is proposed to be integrated with the district forecast differential encoding so as to further improve compression rates. But the spatial resolution of images should be considered. For example, urban areas are characterized by point and linear details inherent to images. If the spatial resolutions of images are relatively higher, the times of edge protection smoothing are supposed not to be too much in order to avoiding large information loss. Otherwise, the land cover types with large redundancy, exemplified by water, would be compressed with a 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 significantly improved compression rate because of the edge protection smoothing. Besides, a sectional drawing is plotted along with the A-A direction, as is shown in Figure 7. The black curve represents the gray values of the A-A section pertaining to the original image, while the red, blue and green curves respectively represent the gray values of the A-A section after once, two times and three times edge protection smoothings. It is clear that the edges are relatively enhanced by the smoothing. Moreover, the positions of edges are relatively permanent. Hence, this kind of smoothing is immune to edge destruction, which will maintain the accuracies of stereo matching. Urban areas | Farmland | Mountain areas | Water Entropy (bits) 6.12 4.89 4.13 3.27 Information amount (bits) 4.31 2.94 2.24 1.65 Average code length 6.16 491 4.17 3.31 Huifinan Coding Compression rate 1.30 1.63 1.92 2.42 Uni-directional Average code length 4.97 3.62 3.18 2.41 differential coding Compression rate 1.61 2.21 2.52 3.32 Bidirectional Average code length 3.93 331 2.78 2.32 differential coding Compression rate 2.04 2.42 2.88 3.45 Table 3. Huffman coding and differential coding of images of four land cover types (b) farmland (c) mountain area Figure 5. Remote sensing images of for typical land cover types (b) (c) (d) water Figure 6. Resulting images after different times of edge protection smoothing: (a) one time; (b) twice; (c) three times

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