SAR圖像的極化干涉非監(jiān)督Wishart分類方法和實驗研究
Study of Polarimetric Interferometric Unsupervised Wishart Classification of SAR Images
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摘要: 該文在合成孔徑雷達圖像的極化非監(jiān)督Wishart分類的基礎上，給出了一種利用極化干涉信息對合成孔徑雷達圖像進行非監(jiān)督分類的方法。該方法主要利用一(66)的極化干涉相關矩陣，從而可以同時考慮單幅圖像的全極化信息以及兩幅像對之間的互相關信息。該文詳細闡述了該方法的具體實現(xiàn)，并利用NASA/JPL的SIR-C/X-SAR系統(tǒng)在中國天山地區(qū)的L波段實測數(shù)據(jù)進行了實驗研究。給出了利用該方法對實驗數(shù)據(jù)進行分類的結果，并與極化非監(jiān)督Wishart分類的結果進行了比較。結果表明，該方法能夠很好地分辨不同類型的地物，保持地物的細節(jié)，并且比極化非監(jiān)督Wishart分類結果有很大改善。Abstract: In this paper, an unsupervised Wishart classification scheme for polarimetric interferometric SAR data sets is introduced. A 6x6 polarimetric interferometric coherency matrix is used, in order to take simultaneously into account the full polarimetric information from one single image and the coherency information between the pair of these images. This classification scheme is applied to full polarimetric interferometric L-band SAR image pairs of Tien Shan, China, acquired by the NASA/JPL SIR-C/X-SAR sensor in 1994. The results show that this scheme can identify different terrain types and at the same time keep thr details of surface features, which improve greatly the results of the polarimetric unsupervised Wishart classification.
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