.png){kind=icon}
Return
Spatial Focused Bitemporal Interactive Network for Remote Sensing Image Change Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2024.3424929.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently, transformers have been widely explored in remote sensing image change detection (RSICD) and achieved remarkable performance. However, most existing transformer-based change detection methods overlook exploring the spatiotemporal relationships between bitemporal images at the features within the same layer, which is crucial for learning discriminative features to perceive changes. Moreover, no explicit spatial constraint has been imposed on the final fused bitemporal features, leading to reduced detection performance on small targets. To address these issues, we propose a spatial focused bitemporal interactive network (SFBI-Net) for RSICD. Specifically, a bitemporal spatiotemporal interactive (BSI) module is proposed, which performs global interactions on bitemporal features at the same network layer and supplements local information to obtain spatiotemporal relationships of bitemporal features for discriminative representation. Furthermore, a spatial focus diversity loss (SFD-Loss) is developed to maximize bitemporal features in the spatial dimension and further enhance the feature representation of change areas, especially small target areas. The experimental results on challenging benchmark datasets demonstrate the superiority of our SFBI-Net. The source code is available at (https://github.com/Mryao-yuan/SFBI-Net).
Keywords:
Feature extraction
Remote sensing
Semantics
Spatiotemporal phenomena
Current transformers
Task analysis
Convolutional neural networks
Bitemporal spatiotemporal interactive (BSI)
remote sensing image change detection (RSICD)
spatial feature focus constraint
transformers
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning
PREAI
ShareSave
ShareSave
View more