.png){kind=icon}
.png){kind=icon}
Return
Frequency-Temporal Attention Network for Remote Sensing Imagery Change Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2024.3477991.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Change detection (CD) in remote sensing imagery is identified as a pivotal task in the field of Earth observation, while it usually confronts the dilemma of intricate data and minor alterations. To address the stated challenge, this letter presents an innovative frequency-temporal attention network for CD (FTAN), which incorporates two advanced modules including the multidimensional convolutional frequency attention module (MCFA) and the interactive attention module (IAM). Specifically, the MCFA module is essential for enhancing sensitivity in CD by merging multiscale spatial and frequency domain features. As a supplement to MCFA, the IAM aggregates category-related tokens and processes cross-attention information from different time phases. The seamless integration of MCFA and IAM empowers the FTAN network with enhanced capabilities to detect minor regions and edges accurately. Experiments on datasets like LEVIR-CD and DSIFN-CD demonstrate superior performance by outperforming existing models in F1 scores and IoU metrics.
Keywords:
Adversarial training
domain adaptation
domain adaptation
hyper- spectral image (HSI) classification
hyper- spectral image (HSI) classification
transfer learning
transfer learning
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K