计算机科学
棱锥(几何)
分割
人工智能
模式识别(心理学)
特征(语言学)
联营
背景(考古学)
图像分割
尺度空间分割
基于分割的对象分类
编码器
计算机视觉
数学
地理
哲学
操作系统
语言学
考古
几何学
作者
Wang Zhi-min,Jiasheng Wang,Kun Yang,Limeng Wang,Fanjie Su,Xinya Chen
标识
DOI:10.1016/j.cageo.2021.104969
摘要
Aiming at solving the problems of inaccurate segmentation of edge targets, inconsistent segmentation of different types of targets, and slow prediction efficiency on semantic segmentation of high-resolution remote sensing images by classical semantic segmentation network, this study proposed a class feature attention mechanism fused with an improved Deeplabv3+ network called CFAMNet for semantic segmentation of common features in remote sensing images. First, the correlation between classes is enhanced using the class feature attention module to extract and process different categories of semantic information better. Second, the multi-parallel atrous spatial pyramid pooling structure is used to enhance the correlation between spaces, to extract the context information of different scales of an image better. Finally, the encoder-decoder structure is used to refine the segmentation results. The segmentation effect of the proposed network is verified by experiments on the public data set GaoFen image dataset (GID). The experimental results show that the CFAMNet can achieve the mean intersection over union (MIOU) and overall accuracy (OA) of 77.22% and 85.01%, respectively, on the GID, thus surpassing the current mainstream semantic segmentation networks.
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