Abstract
In this article, a modified four-component decomposition method with refined volume scattering models is proposed for polarimetric synthetic aperture radar (SAR) image processing. In the new decomposition method, after the orientation angle compensation, the orientation angle is placed in the probability density functions. General forms of the volume scattering models and branch conditions can be obtained. Similar to the four-component scattering power decomposition with extended volume scattering model (S4R) proposed by Sato et al. , refined volume scattering models can be used for various land covers based on the criteria. Since the orientation angles are contained in the refined volume scattering models, the oriented buildings can be discriminated from the vegetation areas and the overestimation problem of volume scattering is substantially overcome. In this article, the performance of the proposed method is evaluated by the spaceborne C-band Gaofen-3 data and airborne L-band E-SAR data. Several approaches are employed as a comparison of the proposed methods. Experimental results show that, compared with the existing decomposition methods, the proposed method can effectively represent the scattering characteristics of the ambiguous regions, and the double-bounce scattering contributions over the urban areas can be substantially enhanced.
Highlights
W ITH the development of high-resolution synthetic aperture radar (SAR) image and measurement technology, SAR can be applied to many remote sensing fields
In the three-component decomposition method proposed by Freeman and Durden (FDD), the coherency matrix can be represented as a weighted sum of three general scattering types [16]
Since the orientation angles are contained in the volume scattering models, this new decomposition method can be used for various landscapes with different orientation angles, and the decomposition results are supported by ground reference
Summary
W ITH the development of high-resolution synthetic aperture radar (SAR) image and measurement technology, SAR can be applied to many remote sensing fields. As one of the important branches of PolSAR, polarimetric target decomposition technology can be used to better understand the target scattering mechanism. The model-based decomposition is directly related to the physical scattering mechanism, which can be constructed based on coherency or covariance matrix. Based on the Y4R, Sato et al [20] decomposed the scattering mechanisms of various land covers with extended volume scattering model (S4R) and obtained better decomposition results. All these approaches decompose the coherency matrix using various volume scattering models, and the volume scattering model is selected based on HH-VV ratio (“magnitude balance”). There comes to a problem that not all the complete polarimetric information from the observed covariance/coherency matrix can be utilized, resulting in the OVS
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