Semantic segmentation is a hot topic in computer vision and various deep learning networks are designed to achieve higher accuracy on that by fully exploring the capability of neural networks. This paper aims to address the issue and proposes the substructures with novelty for popular networks. Meanwhile, we present a cross-channel structure, which simultaneously reduces parameter while the kernel size becomes larger. After that, to overcome the weakness of insufficient dataset which refers to satellite image data, we propose a feature attention mechanism with generative adversarial network to enhance the images' features. We show the recognition result on the satellite image dataset with a large picture. This paper evaluates substructures on the PASCAL VOC2012 dataset and improves the mIOU from 74.68% to 88.15%.

中文翻译：

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图像语义分割的强子结构和特征注意机制

语义分割是计算机视觉中的热门话题，各种深度学习网络旨在通过充分挖掘神经网络的能力来实现更高的准确性。本文旨在解决该问题，并为流行的网络提出新颖的子结构。同时，我们提出了一种跨通道结构，它在内核尺寸变大的同时减少了参数。之后，为了克服参考卫星图像数据的数据集不足的弱点，我们提出了一种具有生成对抗网络的特征注意机制来增强图像的特征。我们用一张大图展示了在卫星图像数据集上的识别结果。本文在 PASCAL VOC2012 数据集上评估子结构，并将 mIOU 从 74.68% 提高到 88.15%。