A Network Combining a Transformer and a Convolutional Neural Network for Remote Sensing Image Change Detection

Remote Sensing

Tao Zhang¹  Guanghui Wang²  Shubi Zhang²  Bin Li² 

[1] Land Satellite Remote Sensing Application Center, MNR, Beijing 100048, China;School of Environmental and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;

关键词: change detection;    transformer;    deep learning;    spatiotemporal feature enhancement;   

DOI  :  10.3390/rs14092228

来源: DOAJ

【 摘 要 】

With the development of deep learning techniques in the field of remote sensing change detection, many change detection algorithms based on convolutional neural networks (CNNs) and nonlocal self-attention (NLSA) mechanisms have been widely used and have obtained good detection accuracy. However, these methods mainly extract semantic features on images from different periods without taking into account the temporal dependence between these features. This will lead to more “pseudo-change” in complex scenes. In this paper, we propose a network architecture named UVACD for bitemporal image change detection. The network combines a CNNs extraction backbone for extracting high-level semantic information with a visual transformer. Here, visual transformer constructs change intensity tokens to complete the temporal information interaction and suppress irrelevant information weights to help extract more distinguishable change features. Our network is validated and tested on both the LEVIR-CD and WHU datasets. For the LEVIR-CD dataset, we achieve an intersection over union (IoU) of 0.8398 and an F1 score of 0.9130. For the WHU dataset, we achieve an IoU of 0.8664 and an F1 score of 0.9284. The experimental results show that the proposed method outperforms some previous state of the art change detection methods.

【 授权许可】

Unknown