Abstract: Rapid acquisition of remote sensing information has important research significance for the development of image semantic segmentation methods in remote sensing image interpretation applications. With more and more types of data recorded by satellite remote sensing images and more and more complex feature information, accurate and effective extraction of information in remote sensing images has become the key to interpret remote sensing images by image semantic segmentation methods. In order to explore the image semantic segmentation method for fast and efficient interpretation of remote sensing images, a large number of image semantic segmentation methods for remote sensing images are summarized. Firstly, the traditional image semantic segmentation methods are reviewed and divided into edge detection-based segmentation methods, region-based segmentation methods, threshold-based segmentation methods and segmentation methods combined with specific theories. At the same time, the limitations of traditional image semantic segmentation methods are analyzed. Secondly, the semantic segmentation methods based on deep learning are elaborated in detail, and the basic ideas and technical characteristics of each method are used as the classification criteria. They are divided into four categories: FCN-based methods, codec-based methods, dilated convolution-based methods and attention-based methods. The sub-methods contained in each type of method are summarized, and the advantages and disadvantages of these methods are compared and analyzed. Then, the common datasets and performance evaluation indexes of remote sensing image semantic segmentation are briefly introduced. Experimental results of classical network models on different datasets are given, and the performance of different models is evaluated. Finally, the challenges of image semantic segmentation methods in high-resolution remote sensing image interpretation are analyzed, and the future development trend is prospected.

Key words: remote sensing images, image semantic segmentation, deep learning semantic segmentation, feature fusion, attention module

摘要： 快速获取遥感信息对图像语义分割方法在遥感影像解译应用发展具有重要的研究意义。随着卫星遥感影像记录的数据种类越来越多，特征信息越来越复杂，精确有效地提取遥感影像中的信息，成为图像语义分割方法解译遥感图像的关键。为了探索快速高效解译遥感影像的图像语义分割方法，对大量关于遥感影像的图像语义分割方法进行了总结。首先，综述了传统的图像语义分割方法，并将其划分为基于边缘检测的分割方法、基于区域的分割方法、基于阈值的分割方法和结合特定理论的分割方法，同时分析了传统图像语义分割方法的局限性。其次，详细阐述了基于深度学习的语义分割方法，并以每种方法的基本思想和技术特点作为划分标准，将其分为基于FCN的方法、基于编解码器的方法、基于空洞卷积的方法和基于注意力机制的方法四类，概述了每类方法中包含的子方法，并对比分析了这些方法的优缺点。然后，简单介绍了遥感图像语义分割常用数据集和性能评价指标，给出了经典网络模型在不同数据集上的实验结果，同时对不同模型的性能进行了评估。最后，分析了图像语义分割方法在高分辨率遥感图像解译上面临的挑战，并对未来的发展趋势进行了展望。

关键词: 遥感图像, 图像语义分割, 深度学习语义分割, 特征融合, 注意力机制