本文選題：變化檢測 + 差異圖像　； 參考：《遙感信息》2017年04期

【摘要】：針對基于差異圖像分類的SAR圖像變化檢測方法中差異圖像保持完整變化區(qū)域難的特點(diǎn),以及按像元分類容易受噪聲的干擾,提出了一種基于綜合差異圖像和按塊k均值聚類法的SAR圖像變化檢測方法。首先,分別通過差值法和對數(shù)比值法得到2幅不同時(shí)相同一地理位置的SAR圖像差異圖像,為了使差異圖像更加平滑和保持邊緣信息,分別對這2種差異圖像進(jìn)行均值濾波和中值濾波。然后,通過簡單的線性結(jié)合得到最終的差異圖像,隨后將差異圖像分成若干個(gè)大小為h×h且不重疊的塊,通過主成分分析提取每個(gè)塊的特征向量,再利用k均值聚類法將特征向量空間分成2類。最后,根據(jù)最近鄰法將差異圖像分為變化區(qū)域和未變化區(qū)域。實(shí)驗(yàn)結(jié)果表明,該方法不僅能有效地檢測出變化區(qū)域,還在一定程度上降低了虛警。
[Abstract]:In order to solve the problem that it is difficult for the difference image to maintain a complete region of change in SAR image change detection method based on differential image classification, the pixel classification is vulnerable to noise interference. A new method of SAR image change detection based on synthetic differential image and block k-means clustering is proposed. Firstly, two different SAR images with the same geographic position are obtained by difference method and logarithmic ratio method, respectively, in order to smooth the difference images and keep the edge information. The two kinds of differential images are filtered by mean value and median filter respectively. Then, the final differential image is obtained by a simple linear combination. Then, the differential image is divided into several blocks of h 脳 h size and no overlap, and the feature vectors of each block are extracted by principal component analysis (PCA). Then the k-means clustering method is used to divide the eigenvector space into two categories. Finally, according to the nearest neighbor method, the difference image can be divided into variable region and unchanged region. The experimental results show that this method can not only detect the region of change effectively, but also reduce the false alarm to a certain extent.
【作者單位】： 中國科學(xué)院遙感與數(shù)字地球研究所;中國科學(xué)院大學(xué);
【分類號】：TN957.52
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本文編號：2112222