Semantic segmentation has been widely used in the basic task of extracting information from images. Despite this progress, there are still two challenges: (1) it is difficult for a single-size receptive field to acquire sufficiently strong representational features, and (2) the traditional encoder-decoder structure directly integrates the shallow features with the deep features. However, due to the small number of network layers that shallow features pass through, the feature representation ability is weak, and noise information will be introduced to affect the segmentation performance. In this paper, an Adaptive Multi-Scale Module (AMSM) and Adaptive Fuse Module (AFM) are proposed to solve these two problems. AMSM adopts the idea of channel and spatial attention and adaptively fuses three-channel branches by setting branching structures with different void rates, and flexibly generates weights according to the content of the image. AFM uses deep feature maps to filter shallow feature maps and obtains the weight of deep and shallow feature maps to filter noise information in shallow feature maps effectively. Based on these two symmetrical modules, we have carried out extensive experiments. On the ISPRS Vaihingen dataset, the F1-score and Overall Accuracy (OA) reached 86.79% and 88.35%, respectively.

中文翻译：

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基于注意力机制和自适应加权的高分辨率遥感影像分割框架

语义分割已广泛用于从图像中提取信息的基本任务中。尽管取得了这一进展，仍然存在两个挑战：（1）单一大小的接收场很难获得足够强的表示特征；（2）传统的编码器-解码器结构直接将浅层特征与深层特征集成在一起。但是，由于浅层特征通过的网络层数量少，特征表示能力较弱，会引入噪声信息影响分割性能。本文提出了一种自适应多尺度模块（AMSM）和自适应保险丝模块（AFM）来解决这两个问题。AMSM采用通道和空间注意的思想，并通过设置具有不同空隙率的分支结构来自适应融合三通道分支，并根据图像内容灵活地生成权重。AFM使用深度特征图对浅层特征图进行过滤，并获取深度和浅层特征图的权重，以有效过滤浅层特征图中的噪声信息。基于这两个对称模块，我们进行了广泛的实验。在ISPRS Vaihingen数据集上，F1得分和总体准确性（OA）分别达到86.79％和88.35％。AFM使用深度特征图对浅层特征图进行过滤，并获取深度和浅层特征图的权重，以有效过滤浅层特征图中的噪声信息。基于这两个对称模块，我们进行了广泛的实验。在ISPRS Vaihingen数据集上，F1得分和总体准确性（OA）分别达到86.79％和88.35％。AFM使用深度特征图对浅层特征图进行过滤，并获取深度和浅层特征图的权重，以有效过滤浅层特征图中的噪声信息。基于这两个对称模块，我们进行了广泛的实验。在ISPRS Vaihingen数据集上，F1得分和总体准确性（OA）分别达到86.79％和88.35％。