融合面向对象和深度置信网络的农村建筑物信息提取

Rural building information extraction using object-oriented and deep belief network

摸清农村建筑物数量、面积和位置等相关信息是合理规划农村土地、建设美丽宜居新时代农村的基础。由于农村建筑物分布较为零散,破碎化程度严重等现象,如何运用遥感技术准确提取零散分布的农村建筑物仍存在挑战。本文提出了一种融合面向对象和深度置信网络的农村建筑物提取方法。首先基于农村建筑物的光谱、形状、纹理等特征,利用面向对象进行尺度分割;然后利用深度置信网络学习不同对象的纹理、环境等高级语义特性,提取农村建筑物信息。与影像分类常用的随机森林模型相比,本文方法在农村建筑物提取中表现较好,提取图斑边缘轮廓较为清晰完整,对于不同建筑物之间的间隙部分也能较好地识别区分,且噪声较少。结果表明该方法能有效提取农村建筑物信息。

Mapping the information related to the number,area and location of rural buildings is the basis for rational planning of rural land and building beautiful and livable rural areas in the new era.However,due to the phenomenon that rural buildings are scattered and severely fragmented,it is still challenging to accurately extract the scattered rural buildings using remote sensing technology.In this study,we propose a rural building extraction method that combines object-oriented and deep confidence networks.Firstly,we use object-oriented scale segmentation based on the spectral,shape and texture features of rural buildings,and further use deep confidence networks to learn high-level semantic features such as texture and environment of different objects to extract rural building information.Compared with random forest commonly used image classification methods,the method in this paper performs better in rural building extraction,with clearer and more complete edge contours of the extracted patches,and better recognition of the distinction between the gap parts between different buildings,and less noise in the extraction results.The method can effectively and efficiently extract rural building information.