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Homogeneous image change detection research has been well developed, and many methods have been proposed. However, change detection between heterogeneous images is challenging since heterogeneous images are in different domains. Therefore, direct heterogeneous image comparison in the way that we do it is difficult. In this paper, a method for heterogeneous synthetic aperture radar (SAR) image and optical image change detection is proposed, which is based on a pixel-level mapping method and a capsule network with a deep structure. The mapping method proposed transforms an image from one feature space to another feature space. Then, the images can be compared directly in a similarly transformed space. In the mapping process, some image blocks in unchanged areas are selected, and these blocks are only a small part of the image. Then, the weighted parameters are acquired by calculating the Euclidean distances between the pixel to be transformed and the pixels in these blocks. The Euclidean distance calculated according to the weighted coordinates is taken as the pixel gray value in another feature space. The other image is transformed in a similar manner. In the transformed feature space, these images are compared, and the fusion of the two different images is achieved. The two experimental images are input to a capsule network, which has a deep structure. The image fusion result is taken as the training labels. The training samples are selected according to the ratio of the center pixel label and its neighboring pixels’ labels. The capsule network can improve the detection result and suppress noise. Experiments on remote sensing datasets show the final detection results, and the proposed method obtains a satisfactory performance.
Wenping Ma; Yunta Xiong; Yue Wu; Hui Yang; Xiangrong Zhang; Licheng Jiao. Change Detection in Remote Sensing Images Based on Image Mapping and a Deep Capsule Network. Remote Sensing 2019, 11, 626 .
AMA StyleWenping Ma, Yunta Xiong, Yue Wu, Hui Yang, Xiangrong Zhang, Licheng Jiao. Change Detection in Remote Sensing Images Based on Image Mapping and a Deep Capsule Network. Remote Sensing. 2019; 11 (6):626.
Chicago/Turabian StyleWenping Ma; Yunta Xiong; Yue Wu; Hui Yang; Xiangrong Zhang; Licheng Jiao. 2019. "Change Detection in Remote Sensing Images Based on Image Mapping and a Deep Capsule Network." Remote Sensing 11, no. 6: 626.
In this paper, a novel change detection approach based on multi-grained cascade forest(gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detectsthe changed and unchanged areas of the images by using the well-trained gcForest. Most existingchange detection methods need to select the appropriate size of the image block. However, thesingle size image block only provides a part of the local information, and gcForest cannot achieve agood effect on the image representation learning ability. Therefore, the proposed approach choosesdifferent sizes of image blocks as the input of gcForest, which can learn more image characteristicsand reduce the influence of the local information of the image on the classification result as well.In addition, in order to improve the detection accuracy of those pixels whose gray value changesabruptly, the proposed approach combines gradient information of the difference image with theprobability map obtained from the well-trained gcForest. Therefore, the image edge information canbe enhanced and the accuracy of edge detection can be improved by extracting the image gradientinformation. Experiments on four data sets indicate that the proposed approach outperforms otherstate-of-the-art algorithms.
Wenping Ma; Hui Yang; Yue Wu; Yunta Xiong; Tao Hu; Licheng Jiao; Biao Hou. Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images. Remote Sensing 2019, 11, 142 .
AMA StyleWenping Ma, Hui Yang, Yue Wu, Yunta Xiong, Tao Hu, Licheng Jiao, Biao Hou. Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images. Remote Sensing. 2019; 11 (2):142.
Chicago/Turabian StyleWenping Ma; Hui Yang; Yue Wu; Yunta Xiong; Tao Hu; Licheng Jiao; Biao Hou. 2019. "Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images." Remote Sensing 11, no. 2: 142.