Song Weigang, Zhong Baojiang, Sun Xun
School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, China.
Sensors (Basel). 2019 Apr 23;19(8):1915. doi: 10.3390/s19081915.
In aerial images, corner points can be detected to describe the structural information of buildings for city modeling, geo-localization, and so on. For this specific vision task, the existing generic corner detectors perform poorly, as they are incapable of distinguishing corner points on buildings from those on other objects such as trees and shadows. Recently, fully convolutional networks (FCNs) have been developed for semantic image segmentation that are able to recognize a designated kind of object through a training process with a manually labeled dataset. Motivated by this achievement, an FCN-based approach is proposed in the present work to detect building corners in aerial images. First, a DeepLab model comprised of improved FCNs and fully-connected conditional random fields (CRFs) is trained end-to-end for building region segmentation. The segmentation is then further improved by using a morphological opening operation to increase its accuracy. Corner points are finally detected on the contour curves of building regions by using a scale-space detector. Experimental results show that the proposed building corner detection approach achieves an F-measure of 0.83 in the test image set and outperforms a number of state-of-the-art corner detectors by a large margin.
在航空影像中，可以检测角点来描述建筑物的结构信息，用于城市建模、地理定位等。对于这个特定的视觉任务，现有的通用角点检测器表现不佳，因为它们无法区分建筑物上的角点与树木和阴影等其他物体上的角点。最近，全卷积网络（FCN）已被开发用于语义图像分割，通过使用手动标注的数据集进行训练过程，能够识别指定类型的物体。受此成果启发，本文提出了一种基于FCN的方法来检测航空影像中的建筑物角点。首先，对由改进的FCN和全连接条件随机场（CRF）组成的DeepLab模型进行端到端训练，用于建筑物区域分割。然后，通过使用形态学开运算进一步改进分割，以提高其准确性。最后，使用尺度空间检测器在建筑物区域的轮廓曲线上检测角点。实验结果表明，所提出的建筑物角点检测方法在测试图像集中的F值为0.83，大大优于许多现有的先进角点检测器。
