基于深度学习的遥感图像语义分割技术研究与应用

Research and Application of Remote Sensing Image Semantic Segmentation Technology Based on Deep Learning

本研究聚焦于改进的DeepLab v3+网络在遥感图像语义分割中的应用,旨在提高遥感图像的语义分割精度。引入了多尺度融合策略以优化分割性能,并通过改进的网络结构和深度可分离卷积来提高特征提取能力。实验结果表明,这一改进方法在Vaihingen和Potsdam数据集上都显著提高了分割准确度。我们的研究有望对地图制图、环境监测等领域产生积极影响。

This study investigates the application of an improved DeepLab v3+network in semantic segmentation of remote sensing images,aiming to improve the accuracy of semantic segmentation of remote sensing images.A multi-scale fusion strategy was introduced to optimize segmentation performance,and feature extraction capabilities were improved through improved network structure and deep separable convolutions.The experimental results show that this improved method significantly improves segmentation accuracy on both Vaihingen and Potsdam datasets.This study is expected to have a positive impact on areas such as map mapping and environmental monitoring.