Adaptive Kalman procedure for SAR high resolution image reconstruction in the planning phase of land consolidation

Authors:
Li Li;Ming Luo;Chao Zhang;Wei Su;Yijun Jiang;Daoliang Li
Affiliations:
College of Information and Electrical Engineering, China Agricultural University, Beijing, P. R. China;Land Consolidataion Center, the Ministry of Land Resources, P. R. China;College of Information and Electrical Engineering, China Agricultural University, Beijing, P. R. China;College of Information and Electrical Engineering, China Agricultural University, Beijing, P. R. China;Land Consolidataion Center, the Ministry of Land Resourses, P. R. China;College of Information and Electrical Engineering, China Agricultural University, Beijing, China
Venue:
WSEAS Transactions on Computers
Year:
2008

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Abstract

Remote Sensing technologies provide the spatial data/maps and offer great advantages for a land consolidation project. But sometimes in some regions, optics and infrared remote sensing can not work well. SAR (Synthetic aperture radar), an active microwave remote sensing imaging radar, has the unique capabilities of obtaining abundant electromagnetic information from ground objects all day/all night and all weather, and penetrating some special objects and detecting the shapes of ground objects. At this point, SAR can meet the requirement. However, for land consolidation application, high spatial resolution SAR images are required. To increase the spatial resolution of SAR images, this work presents a novel approximate iterative and recurrent approach for image reconstruction, namely adaptive Kalman Filter (KF) procedure. Mathematical models and Kalman equations are derived. The matched filter and Kalman Filter are integrated to enhance the resolution beyond the classical limit. Simulated results demonstrate that the method strongly improves the resolution by using prior knowledge, which is a scientific breakthrough in the case that the traditional pulse compression constrains the improvement of SAR spatial resolution. And it is also shown that it is an optimal method in the sense of mean square error and its computation cost is lower than the traditional Kalman Filter algorithm.