基于深度学习的高分辨率遥感影像建筑物提取方法

针对传统的建筑物提取方法精度较低和边界不完整等问题,本文提出基于深度学习的高分辨率遥感影像建筑物提取方法。首先,采用主成分变换非监督预训练网络结构,获得待提取遥感影像特征。其次,为减少在池化过程中影像特征信息的丢失,提出自适应池化模型,通过非下采样轮廓波变换来获取影像纹理特征,并将纹理特征输入网络中参与建筑物提取。最后,将影像特征输入softmax分类器进行分类,获得建筑物提取结果。选取典型区域进行建筑物提取试验,并与典型建筑物提取方法进行对比分析,结果表明,本文提取方法精度高,并且提取建筑物的边界清晰、完整。

A high-resolution remote sensing image building extraction method based on deep learning

Traditional building extraction from very high resolution remote sensing optical imagery is limited by low precision and incomplete boundary. In this paper, a high-resolution remote sensing image building extraction method based on deep learning is proposed. Firstly, Principal Component Analysis is used to pre-train network structure in an unsupervised way and obtain the characteristics of remote sensing image. Secondly, an adaptive pooling model is proposed to reduce the feature information loss in the pooling process. The texture features are extracted by non-subsampled contour wave transformation and introduced to the network to improve the building extraction. Finally, the obtained image features are inputted into the softmax classifier for classification and building extraction results. A typical experiment areas selected. The comparison with typical building extraction method, the experimental results shows that the proposed method can extract the buildings with higher accuracy, especially the clearer and more complete boundary.