Semantic segmentation is an important and foundational task in the application of high-resolution remote sensing images (HRRSIs). However, HRRSIs feature large differences within categories and minor variances across categories, posing a significant challenge to the high-accuracy semantic segmentation of HRRSIs. To address this issue and obtain powerful feature expressiveness, a deep conditional generative adversarial network (DCGAN), integrating fully convolutional DenseNet (FC-DenseNet) and Pix2pix, is proposed. The DCGAN is composed of a generator–discriminator pair, which is built on a modified downsampling unit of FC-DenseNet. The proposed method possesses strong feature expression ability because of its skip connections, the very deep network structure and multiscale supervision introduced by FC-DenseNet, and the supervision from the discriminator. Experiments on a Deep Globe Land Cover dataset demonstrate the feasibility and effectiveness of this approach for the semantic segmentation of HRRSIs. The results also reveal that our method can mitigate the influence of class imbalance. Our approach for precise semantic segmentation can effectively facilitate the application of HRRSIs.

中文翻译：

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具有对抗训练的全卷积DenseNet，用于高分辨率遥感影像的语义分割

语义分割是高分辨率遥感影像（HRRSI）应用中的重要基础任务。但是，HRRSI的特征在于类别之间的巨大差异，而类别之间的差异很小，这对HRRSI的高精度语义分割提出了重大挑战。为了解决这个问题并获得强大的特征表达能力，提出了一种将完全卷积的DenseNet（FC-DenseNet）和Pix2pix集成在一起的深度条件生成对抗网络（DCGAN）。DCGAN由一对生成器－鉴别器组成，该对建立在FC-DenseNet的经过修改的下采样单元上。该方法具有跳过连接，FC-DenseNet引入的非常深的网络结构和多尺度监督等特点，具有很强的特征表达能力，以及歧视者的监督。在Deep Globe Land Cover数据集上进行的实验证明了这种方法对HRRSI进行语义分割的可行性和有效性。结果还表明，我们的方法可以减轻班级失衡的影响。我们用于精确语义分割的方法可以有效地促进HRRSI的应用。