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]

Release time:2021-06-18

Abstract: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.

Indexed by:Journal paper

Translation or Not:no

Included Journals:SCI

Links to published journals:https://ieeexplore.ieee.org/abstract/document/9154568