In remote sensing imagery analysis, patch-based methods have limitations in capturing information beyond the sliding window. This shortcoming poses a significant challenge in processing complex and variable geo-objects, which results in semantic inconsistency in segmentation results. To address this challenge, we propose a dynamic scale perception framework, named GeoAgent, which adaptively captures appropriate scale context information outside the image patch based on the different geo-objects. In GeoAgent, each image patch's states are represented by a global thumbnail and a location mask. The global thumbnail provides context beyond the patch, and the location mask guides the perceived spatial relationships. The scale-selection actions are performed through a Scale Control Agent (SCA). A feature indexing module is proposed to enhance the ability of the agent to distinguish the current image patch's location. The action switches the patch scale and context branch of a dual-branch segmentation network that extracts and fuses the features of multi-scale patches. The GeoAgent adjusts the network parameters to perform the appropriate scale-selection action based on the reward received for the selected scale. The experimental results, using two publicly available datasets and our newly constructed dataset WUSU, demonstrate that GeoAgent outperforms previous segmentation methods, particularly for large-scale mapping applications.

在遥感图像分析中，针对通过滑动窗口局限性的问题，提出了一种动态尺度感知框架GeoAgent，根据不同的地理对象自适应地捕捉图像块外的合适尺度上下文信息，通过全局缩略图和位置蒙版表示每个图像块的状态，利用尺度控制代理进行尺度选择动作来切换分割网络的尺度和上下文分支，实现了多尺度图像块特征的提取和融合，通过调整网络参数来执行合适的尺度选择动作，实验证明GeoAgent在大规模制图应用中优于传统分割方法。

超越补丁: 基于强化学习的高分辨率遥感图像的尺度自适应语义分割