TY - GEN
T1 - DCAFNet
T2 - 25th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024
AU - Cui, Yichen
AU - Shen, Hong
AU - Lam, Chan Tong
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Change detection in bitemporal remote sensing images aids in immediate disaster relief efforts by identifying and analyzing affected areas. However, due to the diversity of backgrounds such as forests, grasslands, and deserts, existing mainstream methods lack generalization when detecting disaster-stricken regions. This leads to issues such as blurry edges and missed detections of small targets. To address these problems, we propose a Change Detection Network (called DCAFNet) that integrates diverse convolutions and attention mechanisms. Based on an encoder-decoder structure, DCAFNet employs a diverse convolution module as the backbone to extract edge features. Additionally, it utilizes a lightweight attention module to enhance the flow of contextual information, improving the detection of small targets. Experiments on a representative landslide dataset validate the capability of DCAFNet. The results show that DCAFNet achieved a Kappa coefficient and F1 score of 81.86%, 91.27% respectively on this dataset, demonstrating the effectiveness of DCAFNet.
AB - Change detection in bitemporal remote sensing images aids in immediate disaster relief efforts by identifying and analyzing affected areas. However, due to the diversity of backgrounds such as forests, grasslands, and deserts, existing mainstream methods lack generalization when detecting disaster-stricken regions. This leads to issues such as blurry edges and missed detections of small targets. To address these problems, we propose a Change Detection Network (called DCAFNet) that integrates diverse convolutions and attention mechanisms. Based on an encoder-decoder structure, DCAFNet employs a diverse convolution module as the backbone to extract edge features. Additionally, it utilizes a lightweight attention module to enhance the flow of contextual information, improving the detection of small targets. Experiments on a representative landslide dataset validate the capability of DCAFNet. The results show that DCAFNet achieved a Kappa coefficient and F1 score of 81.86%, 91.27% respectively on this dataset, demonstrating the effectiveness of DCAFNet.
KW - Attention Mechanism
KW - Change detection
KW - Diversity convolution module
UR - https://www.scopus.com/pages/publications/105002717210
U2 - 10.1007/978-981-96-4207-6_35
DO - 10.1007/978-981-96-4207-6_35
M3 - Conference contribution
AN - SCOPUS:105002717210
SN - 9789819642069
T3 - Lecture Notes in Computer Science
SP - 383
EP - 394
BT - Parallel and Distributed Computing, Applications and Technologies - 25th International Conference, PDCAT 2024, Proceedings
A2 - Li, Yupeng
A2 - Xu, Jianliang
A2 - Zhang, Yong
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 13 December 2024 through 15 December 2024
ER -
