A Distributed Multi-Satellite Collaborative Framework for Remote Sensing Scene Classification

JIN Jing; WANG Feng

doi:10.11999/JEIT250866

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JIN Jing, WANG Feng. A Distributed Multi-Satellite Collaborative Framework for Remote Sensing Scene Classification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250866

Citation:

JIN Jing, WANG Feng. A Distributed Multi-Satellite Collaborative Framework for Remote Sensing Scene Classification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250866

JIN Jing, WANG Feng. A Distributed Multi-Satellite Collaborative Framework for Remote Sensing Scene Classification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250866

Citation:

JIN Jing, WANG Feng. A Distributed Multi-Satellite Collaborative Framework for Remote Sensing Scene Classification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250866

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A Distributed Multi-Satellite Collaborative Framework for Remote Sensing Scene Classification

doi: 10.11999/JEIT250866 cstr: 32379.14.JEIT250866

JIN Jing,
WANG Feng^,

Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China

Funds: Defense Industrial Technology Development Program (JCKY2024110C033)

Received Date: 2025-09-02
Accepted Date: 2025-12-12
Rev Recd Date: 2025-12-11

Available Online: 2025-12-18

Abstract

Abstract

Objective With the rapid development of space technologies, satellites generate large volumes of Remote Sensing (RS) data. Scene classification, a fundamental task in RS interpretation, is essential for earth observation applications. Although Deep Learning (DL) improves classification accuracy, most existing methods rely on centralized architectures. This design allows unified management but faces limited bandwidth, high latency, and privacy risks, which restrict scalability in multi-satellite settings. With increasing demand for distributed computation, Federated Learning (FL) has received growing attention in RS. Research on FL for RS scene classification, however, remains at an early stage. This study proposes a distributed collaborative framework for multi-satellite scene classification that applies efficient parameter aggregation to reduce communication overhead while preserving accuracy. Methods An FL-based framework is proposed for multi-satellite RS scene classification. Each satellite conducts local training while raw data remain stored locally to preserve privacy. Only updated model parameters are transmitted to a central server for global aggregation. The optimized global model is then broadcast to satellites to enable joint modeling and inference. To reduce the high communication cost of space-to-ground links, an inter-satellite communication mechanism is added. This design lowers communication overhead and strengthens scalability. The effect of parameter consensus on global convergence is theoretically analyzed, and an upper bound of convergence error is derived to provide a rigorous convergence guarantee and support practical applicability. Results and Discussions Comparative experiments are conducted on the UC-Merced and NWPU-RESISC45 datasets (Table 2, Table 3) to evaluate the proposed framework. The method consistently shows higher accuracy than centralized training, FedAvg, and FedProx under different client numbers and training ratios. On UC-Merced, Overall Accuracy (OA) reaches 96.68% at a 50% training ratio with 2 clients and rises to 97.49% at 80% with 10 clients. On NWPU-RESISC45, OA reaches 83.64% at 10% with 5 clients and 88.41% at 20% with 10 clients, both exceeding baseline methods. Confusion matrices (Fig. 4, Fig. 5) show clear diagonal dominance and only minor confusions. t-SNE visualizations (Fig. 6) show compact intra-class clusters and well-separated inter-class distributions, indicating strong generalization even under lower training ratios. Communication energy analysis (Table 4) shows high efficiency. On UC-Merced with a 50% training ratio, the communication cost is 1.30 E·kJ, more than 60% lower than FedAvg and FedProx. On NWPU-RESISC45, substantial savings are also observed across all ratios. Conclusions This study proposes an FL-based framework for multi-satellite RS scene classification and addresses limitations of centralized training, including restricted bandwidth, high latency, and privacy concerns. By allowing satellites to conduct local training and applying central aggregation with inter-satellite consensus, the framework achieves collaborative modeling with high communication efficiency. Evaluations on UC-Merced and NWPU-RESISC45 verify the effectiveness of the method. On UC-Merced with an 80% training ratio and 10 clients, OA reaches 97.49%, higher than centralized training, FedAvg, and FedProx by 1.85%, 0.60%, and 0.81%, respectively. On NWPU-RESISC45 with a 20% training ratio, the communication energy cost is 5.88 E·kJ, showing reductions of 57.45% and 58.18% compared with FedAvg and FedProx. These results indicate strong generalization and efficiency across different data scales and training ratios. The framework is suited for bandwidth-limited and dynamic space environments and offers a promising direction for distributed RS applications. Future work will examine cross-task transfer learning to improve adaptability and generalization under multi-task and heterogeneous data conditions.
- Remote Sensing (RS),
- Scene classification,
- Federated Learning (FL),
- Multi-satellite collaboration

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References(26)

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