融合阈值分割和注意力网络的建筑阴影检测

针对高分辨率高层建筑物遥感影像噪声干扰大、阴影检测困难的问题, 本文提出了一种改进阈值分割和注意力残差网络结合的高层建筑物遥感影像阴影检测方法. 首先, 利用改进最大类间和最小类内阈值分割算法建立阈值分割模型, 并基于轮廓间的连通域特性和端点位置约束关系利用欧几里得度量算法对断裂轮廓进行修补得到阴影轮廓; 然后, 利用生成对抗网络模型对误判数据集进行扩充; 最后, 对残差网络进行改进, 在特征图中加入注意力机制进行全局特征融合. 在不同场景下, 分别与辐射模型、直方图阈值分割、彩色模型阴影检测方法, 支持向量机、视觉几何群网络、Inception和残差网络分类网络进行了对比实验, 本文方法综合误判率和漏检率分别为2.1%、1.5%. 结果表明, 本文提出的高层建筑遥感阴影检测算法能较好地完成阴影区域的分割和检测, 有利于节约人力物力资源、协助工作人员进行遥感信息的解译、遥感档案建立等工作, 具有实用价值.

Building Shadow Detection Based on Fusion of Threshold Segmentation and Attention Network

Considering strong noise interference and difficult shadow detection in high-resolution remote sensing images of high-rise buildings, this study proposes a shadow detection method for remote sensing images of high-rise buildings, which is based on the combination of improved threshold segmentation and residual attention networks. Firstly, a threshold segmentation model is built by the improved maximum inter-class and minimum intra-class threshold segmentation algorithm, and on the basis of the connected domain characteristics and end-point positional constraint relationships between contours, the Euclidean metric algorithm is used to repair the broken contours for the shadow contours. Then, the generative adversarial network (GAN) model is used to expand the misjudgment data set. Finally, the residual network is improved, and the attention mechanism is added to the feature map for global feature fusion. In different scenes, the proposed method is compared with the radiation model, histogram threshold segmentation, color model-based shadow detection method, support vector machine (SVM), visual geometry group (VGG) network, Inception, and classification network of residual networks, and the proposed method has a comprehensive misjudgment rate and missed detection rate of 2.1% and 1.5%, respectively. The results reveal that the proposed algorithm can better complete the segmentation and detection of shadow areas, which is conducive to saving human and material resources and assisting staff with their work such as interpreting remote sensing information and establishing remote sensing archives. The proposed method has practical value.