基于深度学习的高分辨率卫星遥感影像围填海检测识别

基于高分辨率卫星遥感影像自动、准确提取围填海土地利用现状，是实现围填海集约使用的重要技术手段。针对高分辨率卫星遥感影像地物特征复杂，依赖人工提取特征的传统方法较难满足业务部门实际需求的问题，提出了基于深度学习的围填海检测识别技术框架，该框架使用U-Net网络的多约束变体结构，并针对高分辨率遥感影像地物特征复杂导致地物分类不一致的问题，引入全连接条件随机场和图像腐蚀运算对分割结果进行后处理。以天津市滨海新区2016年和2020年高分辨卫星遥感影像为数据源进行了验证，实验表明围填海地物分割整体准确率、F1-score、Kappa系数以及mIoU分别达到96.73%、92.87%、90.28%、86.82%。在此基础上，分析提取了该围填海区域土地利用动态变化特征，为围填海集约使用管理提供了有效技术支撑。

Reclamation Detection and Recognition of High Resolution Satellite Remote Sensing Image based on Deep Learning

Based on high-resolution satellite remote sensing images， automatic and accurate extraction of land use status of reclamation is an important technical means to realize the intensive use of reclamation. In view of the complex features of high-resolution satellite remote sensing images， the traditional method of manually extracting features is difficult to meet the actual needs of business departments. A framework of reclamation detection and recognition based on deep learning is proposed. The framework uses the multi constrained variant structure of U-Net network， and to solve the problem of inconsistent classification caused by complex features of high-resolution remote sensing images， full connection conditional random field and image corrosion operation are introduced to post-processing the segmentation results. The high-resolution satellite remote sensing images of Tianjin Binhai New Area in 2016 and 2020 were used as data sources to verify. The experimental results show that the overall accuracy rate， F1 score， kappa coefficient and mIoU of reclamation are 96.73%， 92.87%， 90.28% and 86.82% respectively. On this basis， the dynamic change characteristics of land use in the reclamation area are analyzed and extracted， which provides effective technical support for the intensive use and management of reclamation.