自適應(yīng)SAR圖像邊緣檢測(cè)算法
Adaptive edge detection algorithm of sar image
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摘要: 邊緣檢測(cè)是圖像分析的基礎(chǔ),在對(duì)SAR圖像進(jìn)行邊緣檢測(cè)時(shí),由于SAR圖像存在很強(qiáng)的相干乘性斑點(diǎn)噪聲,幾乎沒(méi)有一種方法既能有效地檢測(cè)邊緣又能排除斑點(diǎn)噪聲的影響而不產(chǎn)生較多的虛假邊緣,特別是在低視數(shù)SAR的情況下,該文指出了在低視數(shù)情況下應(yīng)當(dāng)如何對(duì)Touzi ratio邊緣檢測(cè)方法和最大似然(ML)邊緣方法的檢測(cè)窗口進(jìn)行改進(jìn),在對(duì)SAR圖像進(jìn)行邊緣檢測(cè)時(shí),引入了自適應(yīng)窗口的方法,并將其應(yīng)用到Touzi ratio邊緣檢測(cè)和最大似然 (ML)兩個(gè)恒虛警邊緣檢測(cè)算法中,取得了很好的改進(jìn)效果,引入自適應(yīng)窗口的方法也適用于其它的SAR圖像邊緣檢測(cè)算法。
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關(guān)鍵詞:
- 合成孔徑雷達(dá); 邊緣檢測(cè); 斑點(diǎn); 自適應(yīng)
Abstract: Due to the multiplicative nature of the speckle noise in SAR images, for edge detecting of SAR images, few method can be found to detect edges efficiently while depress the speckle noise effects without too much false edges, especially in the low look number SAR image case. In this paper, discussions are presented on the improvement of Touzi ratio edge detect method and maximum likelihood method in the low look number SAR image case, and then an adaptive window technique is introduced into SAR image edge detection. The adaptive windows are applied to the two Constant False Alarm Ratio (CFAR) edge detection algorithms mentioned above and fine improvement has been achieved. The technique is also proper for other SAR image edge detection algorithms. -
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