Building change detection in remote sensing images with fully convolutional neural network enhanced edge information

doi:10.13474/j.cnki.11-2246.2023.0169

Abstract

Abstract: For many existing deep learning building change detection methods, it is difficult to obtain image structure features, which leads to the problem of low segmentation accuracy of building edge pixels. In this paper, a building change detection model based on enhanced edge information in remote sensing images is proposed. Firstly,the Canny algorithm and the probabilistic Hough transform algorithm are used to extract the linear edge feature map of the building in the bitemporal image as the image structure feature. Then the bi-temporal images and their corresponding edge feature maps are input into a fully convolutional neural network (FCN) that enhances edge information. Finally, the weighted combination function of Dice Loss and CrossEntropy Loss is used to measure the network model. Experiments show that the FCN network with enhanced edge information has certain advantages in accuracy evaluation and visual analysis.

Key words: change detection, buildings, edge information extraction, FCN, WHU dataset

CLC Number:

P237

CHEN Jie, LIU Jiping, XU Shenghua. Building change detection in remote sensing images with fully convolutional neural network enhanced edge information[J]. Bulletin of Surveying and Mapping, 2023, 0(6): 61-67.

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