中南大学
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返回首页Chen J, Zhu J, Sun G, et al. SMAF-Net: Sharing Multiscale Adversarial Feature for High-Resolution Remote Sensing Imagery Semantic Segmentation[J]. IEEE Geoscience and Remote Sensing Letters, 2020.[DOI: 10.1109/LGRS.2020.3011151]
发布时间：2021-06-18
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- 摘要：
- Semantic segmentation of high-resolution remote sensing imagery (HRSI) is a major task in remote sensing analysis. Although deep convolutional neural network (DCNN)-based semantic segmentation models have powerful capacity in pixel-wise classification, they still face challenge in obtaining intersemantic continuity and extraboundary accuracy because of the geo-object's characteristic feature of diverse scales and various distributions in HRSI. Inspired by the transfer learning, in this study, we propose an efficient semantic segmentation framework named SMAF-Net, which shares multiscale adversarial features into a U-shaped semantic segmentation model. Specifically, it uses multiscale adversarial feature representation obtained from a well-trained generative adversarial network to grasp the pixel correlation and further improve the boundary accuracy of multiscale geo-objects. Comparison experiments on the Potsdam and Vaihingen data sets demonstrate that the proposed framework can achieve considerable improvement in the semantic segmentation of HRSI.
- 论文类型：
- 期刊论文
- 是否译文：
- 否
- 收录刊物：
- SCI
- 上一条：Chen J, Yuan Z, Peng J, et al. DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 1194-1205.[DOI:10.1109/JSTARS.2020.3037893]
- 下一条：Chen J, Wan L, Zhu J, et al. Multi-scale spatial and channel-wise attention for improving object detection in remote sensing imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(4): 681-685.[DOI：10.1109/LGRS.2019.2930462]