Mapping oriented geometric quality assessment for remote sensing image compression

Liang Zhai; Xinming Tang; Guo Zhang

doi:10.1117/12.813127

10 November 2008 Mapping oriented geometric quality assessment for remote sensing image compression

Liang Zhai, Xinming Tang, Guo Zhang

Proceedings Volume 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses; 714610 (2008) https://doi.org/10.1117/12.813127
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

Abstract

In satellite mapping application area, geometric quality assessment for remote sensing image compression is of great importance for onboard compression index determination. The paper proposed an integral geometric quality assessment plan for remote sensing image compression, which includes image matching accuracy assessment, effects of compression on automated DSM/DEM extraction, and photogrammetic point determination accuracy assessment. Image matching accuracy analysis shows how degradation in image quality associated with lossy compression can affect matching accuracy. In analyzing effects of compression on automated DSM/DEM extraction, a DSM is extracted from the original stereopair and held as the reference against which the terrain heights obtained from compressed imagery are compared. Similar to DSM extraction accuracy analysis, photogrammetric point determination accuracy analysis is proposed to compare the accuracy of two sets of 3D coordinates of the feature points which are from original images and reconstructed images. The relationship between compression ratio and terrain types was examined. As to SPIHT algorithm adopted in Resources Satellite-3, the experiment results showed that the compression ratio should be no more than 4:1 for mapping application.

Citation Download Citation

Liang Zhai, Xinming Tang, and Guo Zhang "Mapping oriented geometric quality assessment for remote sensing image compression", Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 714610 (10 November 2008); https://doi.org/10.1117/12.813127

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