2020
DOI: 10.3390/rs12132141
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Semantic Segmentation for SAR Image Based on Texture Complexity Analysis and Key Superpixels

Abstract: In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole. Due to speckle noise, traditional over-segmentation algorithm may cause mixed superpixels with different labels. They are usually located adjacent to two areas or contain more noise. In this paper, a new semantic segmentation method of SAR images based … Show more

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Cited by 11 publications
(5 citation statements)
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“…The image histogram is then computed for the produced superpixel image. Finally, the FCM method was applied using histogram parameters to produce the final segmentation [55]. The proposed algorithm is more robust and faster than the conventional FCM and yields better results for color image segmentation.…”
Section: Superpixel-based Fast Fuzzy C-means Algorithmmentioning
confidence: 99%
“…The image histogram is then computed for the produced superpixel image. Finally, the FCM method was applied using histogram parameters to produce the final segmentation [55]. The proposed algorithm is more robust and faster than the conventional FCM and yields better results for color image segmentation.…”
Section: Superpixel-based Fast Fuzzy C-means Algorithmmentioning
confidence: 99%
“…One of the novelties of this proposed algorithm was to determine optimal sub-image size by pattern analysis and another one was optimizing segmentation process by providing the most successful representation of patterns on images. Shang et al [25] proposed a new semantic segmentation method of SAR images based on texture complexity analysis and key superpixels. Complexity analysis was performed and on this basis, mixed superpixels were selected as key superpixels.…”
Section: Related Workmentioning
confidence: 99%
“…Due to the information loss and accuracy reduction caused by the downsampling operation, it is solved by fusing the feature information of the first few layers of the network and optimising the output result, but the improvement effect is limited [22]. At present, it is mainly improved by memorising the position index of the selected element in the maximum pooling layer and then combining them in the decoder part to generate an upsampling map.…”
Section: Encoder-decoder Structure Of Erfnet Networkmentioning
confidence: 99%