摘要：In this paper a three component model-based decomposition with adaptive selection of unitary transformations for polarimetric synthetic aperture radar (POLSAR) data processing is proposed. Singh et al implemented two unitary transformations on the coherency matrix to minimize the power of cross-polarization, and as a result the T23element of the coherency matrix becomes zero. Another two unitary transformations are proposed by us to carry out on the coherency matrix also to minimize the power of crosspolarization, and the T13element of the coherency matrix becomes zero. Here, we first implement Singh's two unitary transformations and the proposed two unitary transformations on the coherency matrix separately. Then we select the one which leads to the smaller T33. At last, we carry out the three component model-based decomposition proposed by Freeman and Durden based on the obtained coherency matrix. The smaller T33is obtained, the better the over-estimation of volume scattering in model-based decomposition can be suppressed. The RADARSAT-2 POLSAR data of San Francisco area is used to validate the improvement of the proposed method over the three component decomposition only with Singh's two unitary transformations.
摘要：In PolSAR data processing, deorientation operation is often necessary. The existing deorientation method uniformly deorients all the sub-scatterers of a resolution cell with one orientation angle. For high entropy situation, the sub-scatterers have diverse OAs, and the effect of the existing method is unsatisfactory. A novel deorientation method is proposed to well treat the high entropy situation. Cloude's eigen-decomposition to the coherency matrix is first carried out. The three eigenvectors are then separately deoriented with their own orientation angles. Experiments demonstrate that the proposed method is suitable for extraction of urban regions, especially for extraction of oriented urban regions. �VDE VERLAG GMBH �Berlin �Offenbach.
摘要：Man-made buildings detection is important in land use supervision and land control applications. Generally, polarimetric synthetic aperture radar (PolSAR) data are processed to detect buildings well. But for some buildings which are not aligned with the radar track, these buildings are usually incorrectly recognized as forest, because the oriented buildings produce additional cross-polarization. Polarimetric interferometric SAR (PolINSAR) acquires two measurements with a spatial baseline or a temporal baseline. For the PolINSAR with a temporal baseline i.e., the repeat pass PolInSAR, the two polarimetric measurements are sensitive to targets' temporal variations during the time. The buildings, regardless of the orientations, have high coherence, while some natural targets have low coherence. A novel parameter is proposed here, which represents the mean PolINSAR coherence and can be utilized to distinguish between buildings and some natural targets. The parameter is based on the coherence optimization theory of Cloude and Papathanassiou, and is the mean of the three optimal coherences with three pseudo-probabilities. Based on this new parameter and the SPAN, a method to detect buildings is further proposed. The excellent performance of the proposed method on buildings extraction is demonstrated by processing German Aerospace Center (DLR) L-band E-SAR repeat pass PolINSAR data of Oberpfaffenhofen area.
摘要：Cloude-Pottier incoherent target decomposition (ICTD) and Touzi ICTD has been widely applied as a popular approach to interpret the scattering characteristics of a target in polarimetric synthetic aperture radar (PolSAR) data processing. However, they have a common drawback, i.e. proliferation of parameters (PoP) is unavoidable. Paladini et al. solved this problem by developing an orientation-invariant ICTD based on the coherency matrix under circular polarization basis. As an alternative to Paladini decomposition, we proposed a novel ICTD based on the frequently used coherency matrix under linear polarization basis. The proposed method can also avoid the problem of PoP, and avoid the ambiguity of alpha angle of Paladini decomposition as well. Real PolSAR data is processed to validate the proposed decomposition.
摘要：The model-based decomposition that originated from Freeman-Durden three-component decomposition (FDD) has been widely applied in polarimetric synthetic aperture radar (PolSAR) data processing for its clear physical interpretation and easy implementation. Numerous improvements have been proposed to settle the twomain drawbacks of FDD, i.e., the incomplete utilization of the polarimetric information in the coherency matrix and the negative scattering power problem. Recently, Cui et al. proposed a complete model-based three-component decomposition which successfully settled the two aforementioned drawbacks. However, the three scattering components' powers are not totally derived using scattering models, and the remaining coherency matrix (RCM) obtained by subtracting the volume scattering component from the coherency matrix is not consistent with the models of surface and double-bounce scattering components. As an extension of Cui's method, this letter is dedicated to develop a novel method to discriminate the surface and double-bounce scattering components both using scattering models. With the orientation angle (OA) variation and helix angle (HA) variation compensated for the RCM, the RCM is automatically consistent with the models of surface and double-scattering components. The OA variation and HA variation compensation for the RCMis done by unitary transformations of the eigenvectors of the RCM. The powers of surface and double-bounce scattering components are positive. The effectiveness of the proposedmethod is demonstrated by processing the real PolSAR data.