Siamese High-Resolution Network for Change Detection

Yu Wang, Jia Liu, Fang Liu, Wenhua Zhang, Liang Xiao, Jiao Shi

Abstract: Deep learning for change detection can provide effective guidance in many applications, such as agricultural development, urban planning, disaster avoidance, etc. In this study, a Siamese deep learning network based on High-Resolution Network (HRNet) is proposed to generate accurate results. HRNet can integrate multi-dimensional features and output high-resolution results which have attracted attention due to its reliable feature extraction ability. In this paper, we extract the feature pairs of several different dimensions, including the two features behind the down-sampling in the stem stage which is an important part of HRNet. Moreover, feature ex-traction and intensive up-sampling tasks are completed by using a variety of feature fusion sub-networks, which are used to enhance the learning ability. Experiments show the superiority of the proposed Siamese HRNet on a widely used change detection dataset.

External IDs:dblp:conf/igarss/WangLLZXS22