Paper Info
Title
A Novel Approach to Processing Very-High-Resolution Spaceborne SAR Data With Severe Spatial Dependence
Abstract
An innovative approach, mainly featured by progressive iteration of partitioning and focusing, to processing very-high-resolution spaceborne synthetic aperture radar (SAR) data is presented in this article. Due to the long integration time endured during data acquisition as well as large scene extensions being common to the advanced SAR systems, spatial dependence of range histories may be severe and must be properly dealt with. In this article, after deriving the exact two dimensional spectrum for an curved satellite orbit, we focus the entire echo data through several iterations of partitioning and focusing. By partitioning, each partitioned data block can be better focused by the following focusing step, and meanwhile, focusing with better quality enables the following partitioning with both higher granularity and less overlapped data region. Besides, a new technique aiming at eliminating spatial dependence inside a data block is also presented and deployed in the processing procedure. Owing to the feature of high granularity partitioning, the spatial dependence of many factors can be accommodated finer and easier compared with other conventional algorithms. What is more, the artifacts caused by subaperture recombination are fundamentally avoided by the fact that the partitioned blocks are not recombined until being processed into fully focused image blocks. Finally, the precision and the efficiency of the proposed methodology are validated by results on a simulated and a real SAR data.
Year
DOI
Venue
2022
10.1109/JSTARS.2022.3202932
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Keywords
DocType
Volume
Focusing, Synthetic aperture radar, History, Spaceborne radar, Orbits, Fitting, Data models, Image formation, spatial dependence, synthetic aperture radar (SAR), very high resolution
Journal
15
ISSN
Citations 
PageRank 
1939-1404
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Dadi Meng100.34
Lijia Huang2316.40
Xiaolan Qiu300.34
Guangzuo Li401.01
Yuxin Hu562.64
Bing Han614.88
Donghui Hu79416.30