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
点击次数：
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
发布期刊链接：https://ieeexplore.ieee.org/abstract/document/9154568
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]