Abstract
With the development of polarimetric synthetic aperture radar (PolSAR), quantitative parameter inversion has been seen great progress, especially in the field of soil parameter inversion, which has achieved good results for applications. However, PolSAR data is also often many terabytes large. This huge amount of data also directly affects the efficiency of the inversion. Therefore, the efficiency of soil moisture and roughness inversion has become a problem in the application of this PolSAR technique. A parallel realization based on a graphics processing unit (GPU) for multiple inversion models of PolSAR data is proposed in this paper. This method utilizes the high-performance parallel computing capability of a GPU to optimize the realization of the surface inversion models for polarimetric SAR data. Three classical forward scattering models and their corresponding inversion algorithms are analyzed. They are different in terms of polarimetric data requirements, application situation, as well as inversion performance. Specifically, the inversion process of PolSAR data is mainly improved by the use of the high concurrent threads of GPU. According to the inversion process, various optimization strategies are applied, such as the parallel task allocation, and optimizations of instruction level, data storage, data transmission between CPU and GPU. The advantages of a GPU in processing computationally-intensive data are shown in the data experiments, where the efficiency of soil roughness and moisture inversion is increased by one or two orders of magnitude.
Highlights
Soil moisture and roughness are important parameters in the fields of agriculture, ecology, meteorology, and hydrology
There is no accuracy loss after these algorithms are implemented on graphics processing unit (GPU)
The test site is the Demmin area in northern Germany, and the real polarimetric synthetic aperture radar (PolSAR) data is acquired by the ESAR airborne system of the German Aerospace Center
Summary
Soil moisture and roughness are important parameters in the fields of agriculture, ecology, meteorology, and hydrology. In the process of ground detection, the anti-interference of SAR is very low, and the detection process is affected Both GIS and remote sensing assistance information are used for soil moisture estimation [11]. Soil parameter estimation is a representative quantitative application of PolSAR data, since it covers both geometrical and physical parameters. This paper studies the Dubois, Oh, and X-Bragg model inversion algorithms, which basically covers all widely used empirical models. If we want to retrieve the soil roughness and moisture from the data, the corresponding inversion algorithms are needed. We mainly analyze the optimization of the three algorithms for soil inversion based on the GPU parallel method.
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