深度学习遥感影像解译技术在耕地保护中的应用  

Application of deep learning remote sensing image interpretation technology in cultivated land protection

作　　者：张戬[1] 高雅 ZHANG Jian;GAO Ya(Jiangsu Institute of Surveying and Mapping,Nanjing 210000,China)

机构地区：[1]江苏省测绘研究所,江苏南京210000

出　　处：《测绘通报》2023年第8期142-145,共4页Bulletin of Surveying and Mapping

摘　　要：耕地保护事关国家粮食安全、生态安全和社会稳定,是国计民生的头等大事。深度学习技术在海量数据分析领域的广泛应用,为高效、精准的遥感影像解译提供了技术基础。本文研究了基于深度学习的遥感影像解译技术,利用遥感影像数据和对应的矢量数据构建了可供训练的解译样本库,并提出了一种基于深度残差网络结构的解译模型。通过试验证明了该模型的实用性,实现了对试验区域主要地类面积的变化监测,对比多期影像解译结果和增减挂钩等业务数据,验证了耕地复垦的实施情况。结果表明,深度学习遥感影像解译技术在耕地保护领域有较广泛的应用前景。Cultivated land protection is related to national food security,ecological security and social stability.The wide application of deep learning technology in the field of massive data analysis provides a technical foundation for efficient and accurate remote sensing image interpretation.In this paper,the remote sensing image interpretation technology based on deep learning is studied,and a sample library of interpretation samples for training is constructed by using remote sensing image data and corresponding vector data,and an interpretation model based on deep residual network structure is proposed.The practicality of the model has been proved by experiments.In this paper,the change of the main land area in the experimental area is monitored,and the implementation of cultivated land reclamation is verified by comparing the multi-phase image interpretation results and the business data such as the increase and decrease linkage.The results show that deep learning remote sensing image interpretation technology has a wide application prospect in the field of cultivated land protection.

关 键 词：耕地保护 深度学习 高分辨率遥感影像 

分 类 号：P237[天文地球—摄影测量与遥感]