分类: 地球科学 >> 空间物理学 提交时间: 2017-03-10
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.
分类: 地球科学 >> 空间物理学 提交时间: 2017-03-10
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.