.png){kind=icon}
.png){kind=icon}
Return
TeCCo: A Terminal-Cloud Cross-Domain Collaborative Framework for Remote Sensing Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2024.3515464.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The terminal-cloud collaborative framework boosts precision and efficiency by integrating cloud computing power with low-latency terminal responsiveness, offering a suitable solution for the growing demands of multiplatform remote sensing (RS) image interpretation. However, the significant differences in data distribution across various RS platforms present a great challenge in balancing the cloud's centralized processing capabilities with the local interpretation abilities of different terminals. To address this challenge, we propose a terminal-cloud cross-domain collaborative (TeCCo) framework that inherits the efficiency advantages of multiple platforms while ensuring high-accuracy interpretation of diverse data distributions from different terminals. First, the dual classifier co-learning (DCCL) module is designed to enhance cloud robustness. By combining a multilayer perceptron for instance-level classification and a graph convolutional network (GCN) for feature-level aggregation, it achieves mutual supervision and improves feature alignment across different data distributions. Second, the hypernetwork personalization (HNP) module is introduced to generate personalized classifier parameters for each terminal with little fine-tuning cost, allowing terminals to maintain their uniqueness while benefiting from the generalization advantages of collaborative training. Finally, a data-assisted progressive inference mechanism is proposed to enhance accuracy by jointly clustering the features transmitted from terminals and the features of supervised data in the cloud. Extensive experiments demonstrate that TeCCo effectively addresses data distribution challenges, enhancing both the generalization of the cloud model and the personalization of terminal models, achieving state-of-the-art (SOTA) performance in cross-domain and multiplatform RS image classification.
Keywords:
Collaboration
Training
Cloud computing
Accuracy
Data models
Adaptation models
Image classification
Robustness
Inference mechanisms
Feature extraction
Domain adaptation
multiplatform image classification
remote sensing (RS)
terminal-cloud collaboration
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
Organization
No organization information available