Remote sensing image change detection (RSCD) holds significant application value in fields such as environmental monitoring, post-disaster assessment, and urban planning. However, existing deep learning methods often face challenges of high computational complexity and insufficient detail capture, particularly demonstrating limited performance in detecting high-resolution images and complex change regions. To address these issues, this paper proposes a novel network architecture, TIMA-Net, which is designed for efficient remote sensing image change detection. By introducing the timing interaction enhancement module and the progressive decoder based on multi-scale fusion, TIMA-Net improves the accuracy of change detection while ensuring efficient computing performance. Specifically, TIMA-Net designs a temporal interaction enhancement module based on dual-branch and coordinate attention and combines channel segmentation and multi-scale features to enhance the representation ability of the changed region. The comparative experimental results show that TIMA-Net outperforms several state-of-the-art methods on multiple remote sensing datasets, especially in terms of accuracy and computational efficiency. The ablation experiment results show that each module contributes to the final performance. In summary, TIMA-Net not only provides an efficient and accurate remote sensing image change detection solution but also shows its strong potential and broad application prospects in practical applications.

Semantic segmentation of high-resolution remote sensing images is of great application value in fields like natural disaster monitoring. Current multimodal semantic segmentation methods have improved the model’s ability to recognize different ground objects and complex scenes by integrating multi-source remote sensing data. However, these methods still face challenges such as blurred boundary segmentation and insufficient perception of multi-scale ground objects when achieving high-precision classification. To address these issues, this paper proposes MMA-Net, a semantic segmentation network enhanced by two key modules: cross-layer multimodal fusion module and multi-scale multi-attention module. These modules effectively improve the model’s ability to capture detailed features and model multi-scale ground objects, thereby enhancing boundary segmentation accuracy, detail feature preservation, and consistency in multi-scale object segmentation. Specifically, the cross-layer multimodal fusion module adopts a staged fusion strategy to integrate detailed information and multimodal features, realizing detail preservation and modal synergy enhancement. The multi-scale multi-attention module combines cross-attention and self-attention to leverage long-range dependencies and inter-modal complementary relationships, strengthening the model’s feature representation for multi-scale ground objects. Experimental results show that MMA-Net outperforms state-of-the-art methods on the Potsdam and Vaihingen datasets. Its mIoU reaches 88.74% and 84.92% on the two datasets, respectively. Ablation experiments further verify that each proposed module contributes to the final performance.

With the rapid advancement of deep learning, substantial progress has been achieved in remote sensing change detection (CD). However, there are still two key challenges. First, the widespread scene context interference hinders the accurate detection of change regions; second, the existing methods are difficult to simultaneously detect change regions across different scales. To address these issues, this paper presents a cross-spatio-temporal weight adjustment network (CWA-Net) with three core optimizations. First, we propose a cross-spatio-temporal differential fusion attention (CDFA) mechanism, which utilizes differential features extracted by the backbone network to enhance bitemporal features. Through the coordinated use of multiple attention mechanisms and channel exchange, the mechanism promotes deep interaction and fusion of bitemporal features, effectively reinforcing change region representations while mitigating scene interference. Second, we design a multi-scale selection and aggregation module (MSAM) that adaptively selects and aggregates the optimal scale features from multi-scale features, enhancing the model's capability to capture change regions at different scales. Additionally, we put forward a two-type change-feature complementarity (T $^{2}$ CFC) strategy, which reweights change features extracted via subtraction and concatenation during the aggregation of multi-scale feature maps, thereby enhancing feature complementarity and enriching change information. Finally, extensive experiments on four remote sensing CD datasets demonstrate that CWA-Net, based on a simple backbone network ResNet18, outperforms existing state-of-the-art SOTA methods.