7 November 2008 A comparison of several raw data compression algorithms for acquisition of remotely sensed data
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Proceedings Volume 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images; 71470L (2008) https://doi.org/10.1117/12.813222
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
As an active microwave sensor, synthetic aperture radar (SAR) is capable of continuously monitoring geophysical parameters related to the structural and electrical properties of the earth's surface and subsurface. With the development of advanced SAR technologies with high resolutions and multiple imaging modes, SAR generates a large amount of remotely sensed data to be transmitted and processed. The raw data compression has become important tools to reduce the huge amount of data for downlink and required memory on-board. In this paper, four compression algorithms are discussed, including block adaptive quantization (BAQ) algorithm, amplitude and phase compression (AP) algorithm, wavelet BAQ (WT-BAQ) algorithm and wavelet packet BAQ (WPT-BAQ) algorithm. Considering the statistical independent property between amplitude and phase of raw data along with the growing popularity of wavelets, two additional algorithms are presented: wavelet AP (WT-AP) algorithm and wavelet packet AP (WPT-AP) algorithm. The six different algorithms are compared in the signal domain and the image domain with several quality parameters and the simulation is given to validate analytic result. The experimental results will be used for remotely sensed data acquisition, data processing and SAR systems design.
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Mei Zhou, Mei Zhou, Yunkai Deng, Yunkai Deng, Zhimin Zhang, Zhimin Zhang, Lingli Tang, Lingli Tang, Chuanrong Li, Chuanrong Li, } "A comparison of several raw data compression algorithms for acquisition of remotely sensed data", Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71470L (7 November 2008); doi: 10.1117/12.813222; https://doi.org/10.1117/12.813222
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