Ultrahigh resolution (UHR) remote sensing image segmentation aims to achieve a fine-grained understanding of complex ground scenes. In recent years, vision foundation models (VFMs) have shown strong capability in learning generic structural priors from large-scale visual data, indicating great potential for such fine-grained scene understanding. However, their application to UHR remote sensing images remains limited, as the massive parameter scales of VFMs are difficult to train under UHR remote sensing images. Motivated by the success of the parameter-efficient fine-tuning paradigm on VFMs, we propose a novel parameter-efficient local-context cooperation (PEACE) framework, which significantly reduces trainable parameter overhead while improving segmentation accuracy. In particular, PEACE leverages a shared VFM with minimal trainable parameters to collaboratively process local and corresponding contextual patches partitioned from the UHR remote sensing image. A multireceptive local adapter (MRLA) and a multireceptive context adapter (MRCA) are designed to capture spatial features of local and contextual inputs across multiple receptive fields. Finally, contextual semantics are integrated into local representations. Furthermore, a context-sensitive assistance strategy (CSAS) leverages the correct prediction of the context to effectively overcome the primary limitations of the patch-based training paradigm. Experimental results demonstrate that PEACE effectively exploits VFMs and remote sensing foundation models (RSFMs) with minimal parameter increments and achieves versatility and superior performance across several UHR remote sensing image benchmarks.
Qi Li, Chun-Ju Chen, Jiaxin Cai et al.· IEEE Transactions on Geoscie...· 0 citations
Integrated Sensing and Communication (ISAC) is emerging as a key technology for next-generation wireless networks, enabling simultaneous communication and sensing functionalities. This paper focuses a RIS-assisted full-duplex (FD) ISAC system, in which a multi-antenna base station (BS) concurrently performs multi-user uplink and downlink transmission while also carrying out radar sensing. To maximize the joint uplink–downlink sum rate, an optimization problem is formulated under practical constraints, such as radar detection SINR, self-interference, BS transmit power, user power budgets, and RIS unit-modulus conditions. To address the nonconvexity of this problem, a two-stage hybrid optimization approach is developed. In the first stage, the augmented Lagrangian technique decomposes the complex problem into simpler subproblems involving beamforming, power allocation, and RIS phase optimization, leading to a feasible initial solution. The second stage employs a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to refine this solution adaptively, enabling the system to respond effectively to variations in the channel environment, mobility patterns, and interference levels. The proposed hybrid framework achieves optimal resource allocation while maintaining feasibility, robustness, and adaptability. Analytical results confirm its convergence behavior, and extensive simulation results confirm that the proposed scheme consistently outperforms conventional optimization and single-agent DRL baselines in sum-rate maximization, interference mitigation, and sensing accuracy, confirming its effectiveness for RIS-assisted full-duplex ISAC systems.
S. Waqas, Fenghua Huang, Fakhar Abbas et al.· IEEE Transactions on Wireles...· 0 citations