人工智能
合成孔径雷达
计算机科学
像素
散斑噪声
斑点图案
模式识别(心理学)
计算机视觉
噪音(视频)
变更检测
小波
雷达成像
雷达
图像(数学)
电信
作者
Feng Gao,Junyu Dong,Bo Li,Qizhi Xu
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2016-12-01
卷期号:13 (12): 1792-1796
被引量:204
标识
DOI:10.1109/lgrs.2016.2611001
摘要
This letter presents a novel change detection method for multitemporal synthetic aperture radar images based on PCANet. This method exploits representative neighborhood features from each pixel using PCA filters as convolutional filters. Thus, the proposed method is more robust to the speckle noise and can generate change maps with less noise spots. Given two multitemporal images, Gabor wavelets and fuzzy c-means are utilized to select interested pixels that have high probability of being changed or unchanged. Then, new image patches centered at interested pixels are generated and a PCANet model is trained using these patches. Finally, pixels in the multitemporal images are classified by the trained PCANet model. The PCANet classification result and the preclassification result are combined to form the final change map. The experimental results obtained on three real SAR image data sets confirm the effectiveness of the proposed method.
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