矢量边界约束下基于深度学习和高分影像的土地利用矢量图斑变化检测方法
Vector Boundary Constrained Land Use Vector Polygon Change Detection Method based on Deep Learning and High-resolution Remote Sensing Images
史嘉诚 1刘伟 1尹鹏程 2曹兆峰 3王云凯 3单浩宇 1张启华3
作者信息
- 1. 江苏师范大学 地理测绘与城乡规划学院,江苏 徐州 221116
- 2. 徐州市自然资源和规划局,江苏 徐州 221006
- 3. 江苏省地质测绘院,江苏 南京 211102
- 折叠
摘要
土地利用矢量图斑是反映土地利用状况和空间分布的重要数据.然而,随着城市化进程的加快,以人工为主的方法已无法满足当前精准、高效的土地利用矢量图斑变化检测需求.因此,本文结合深度学习技术,并考虑其对样本量的需求,提出了一种矢量边界约束下的土地利用矢量图斑自动变化检测方法.首先,在T1时期的矢量图斑约束指导下,利用改进的简单线性迭代聚类算法对两期高分辨率影像进行精确分割.其次,通过基于超像素的自动样本生成和纯化技术,构建了高质量的数据集.然后,应用改进的双线性卷积神经网络对T2时期的影像进行分类.最后,通过统计和分析T1时期矢量图斑内土地利用类型的变化比例,得出土地利用矢量图斑的变化检测结果.实验区位于无锡市惠山区洋溪河区域,本文方法的精确率和召回率分别达到了 87.2%和96.1%,优于基于矢量图斑特征统计和基于变化像元统计的方法.证明本文方法能够精准、自动定位变化的土地利用矢量图斑,在遏制耕地"非农化"、检查违法建筑等方面展现出了广阔的应用前景.
Abstract
Land use vector polygons serve as crucial data reflecting the status and spatial distribution of land use.However,with the acceleration of urbanization,manual-based methods can no longer meet the current de-mands for precise and efficient detection of changes in land use vector polygons Therefore,this paper combines deep learning technology,considering its requirement for a large volume of samples,and proposes an automatic change detection method for land use vector polygons under vector boundary constraints.Firstly,an improved simple linear iterative clustering algorithm is guided by the T1 vector polygons to accurately segment two peri-ods of high-resolution imagery.Secondly,a high-quality dataset is constructed using an automatic sample gen-eration and purification technique based on superpixels.Subsequently,an improved bilinear convolutional neural network is applied for T2 image classification.Finally,by statistically analyzing the proportion of land use type changes within the T1 vector polygons,the change detection results for land use vector polygons are derived.The experimental area is located in the Yangxi River area of Huishan District,Wuxi City,where the precision and recall rates of our method reached 87.2% and 96.1%,respectively,outperforming methods based on vector polygon feature statistics and change pixel statistics.This demonstrates the ability of our method to accurately and automatically locate changed land use vector polygons,showing broad application prospects in curbing the"non-agriculturalization"of arable land and inspecting illegal buildings.
关键词
土地利用矢量图斑/超像素/高分遥感影像/变化检测/深度学习Key words
Land use vector polygons/Superpixels/High-resolution remote sensing image/Change detection/Deep learning引用本文复制引用
基金项目
在徐高校服务"343"产业发展项目(gx2023010)
江苏省自然资源厅科技创新项目(2021046)
江苏省地质矿产勘查局科研项目(2020KY11)
江苏高校优势学科建设工程资助项目()
资源与环境信息系统国家重点实验室开放基金项目()
江苏师范大学研究生科研创新计划项目(2021XKT0096)
出版年
2024
NETL
NSTL
万方数据