TY - GEN
T1 - HEANet
T2 - 2nd World Conference on Intelligent and 3D Technologies, WCI3DT 2023
AU - Mu, Feng
AU - Pan, Yongzhuo
AU - Li, Jianan
AU - Qin, Haolin
AU - Shen, Ning
AU - Xu, Xin
AU - Chen, Zhenxiang
AU - Xu, Tingfa
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Change detection enables the detection of changes in objects from multi-temporal images. Recently, deep learning plays an important role in the field of change detection. Current methods perform multi-stage feature extraction from the input images to obtain high-level and low-level features, but ignoring the relationship between high-level features and low-level features. To deal with the above problem, this paper proposes a hierarchical-feature enhanced attention Network (HEANet), which integrates a hierarchical-feature enhanced attention (HEA) module for strengthening the association of hierarchical-feature and an adaptive scale enhancement (ASE) module for better feature representation. Extensive experiments show that our method achieves state-of-the-art performance compared to other methods on SYSU dataset.
AB - Change detection enables the detection of changes in objects from multi-temporal images. Recently, deep learning plays an important role in the field of change detection. Current methods perform multi-stage feature extraction from the input images to obtain high-level and low-level features, but ignoring the relationship between high-level features and low-level features. To deal with the above problem, this paper proposes a hierarchical-feature enhanced attention Network (HEANet), which integrates a hierarchical-feature enhanced attention (HEA) module for strengthening the association of hierarchical-feature and an adaptive scale enhancement (ASE) module for better feature representation. Extensive experiments show that our method achieves state-of-the-art performance compared to other methods on SYSU dataset.
KW - Change detection
KW - Enhanced hierarchical feature
KW - Remote sensing image processing
UR - https://www.scopus.com/pages/publications/85203602553
U2 - 10.1007/978-981-97-2144-3_28
DO - 10.1007/978-981-97-2144-3_28
M3 - Conference contribution
AN - SCOPUS:85203602553
SN - 9789819721436
T3 - Smart Innovation, Systems and Technologies
SP - 375
EP - 384
BT - AI Methods and Applications in 3D Technologies - Proceedings of 3DWCAI 2023
A2 - Kountchev (Deceased), Roumen
A2 - Patnaik, Srikanta
A2 - Wang, Wenfeng
A2 - Kountcheva, Roumiana
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 May 2023 through 28 May 2023
ER -