With the continuous growth of the population and the rapid development of the global economy, increasing human activities are driving land-cover utilization changes. Timely and accurate understanding of these changes is crucial for national economic construction, social development, and ecological protection. The use of multi-temporal remote sensing images to detect land cover changes, continuously update national land survey results, and maintain the accuracy and current status of basic geographic information is essential for intelligent change detection methods. However, existing land cover change detection is susceptible to the influence of light and seasonal variations, leading to pseudo-changes and misdetection or omission in change detection results. To address this, we design a remote sensing image change detection method based on adaptive boundary sensing. Convolutional neural networks (CNNs) excel at extracting local features, while Transformer is more advantageous in global feature extraction. Our method adopts a hybrid CNN and Transformer structure for feature extraction, combining edge information to enhance change detection sensitivity, providing more accurate results and improving the model resistance to external conditions such as light and seasonal interference.

During the encoding stage, res2net is employed as an encoder to extract multiscale features and enhance variation features through a difference enhancement module, reducing redundant feature interference. In the decoding stage, a boundary extractor using deformable convolution obtains precise semantic boundary features. These edge features guide the Transformer for contextual information aggregation. Finally, a multi-scale fusion output strategy integrates different scale feature maps, adding multiple connections between decoders of varying levels to fuse low-level spatial information with high-level semantic information, achieving contextual information aggregation, generating the predicted change map, and completing the change detection task.

To validate our method’s effectiveness, experiments are conducted on two public datasets: ① the CLCD dataset, comprising 600 cropland change sample image pairs collected by Gaofen-2 satellites over Guangdong Province in 2017 and 2019, with resolutions ranging from 0.5 to 2 m; ② the RSCD dataset is publicly from the 2022 Aerospace Hongtu Cup Remote Sensing Image Intelligent Processing Algorithm Competition, consisting of 3000 image pairs from Gaofen-1 and Gaofen-2 with 0.8 m to 2 m resolution. On these two datasets, our method achieves F₁ scores of 72.82% and 58.96%, respectively. Meanwhile, visualization results also indicate better performance in recognizing both small and large area changes, with continuous boundaries and complete detection areas. Our method’s change maps closely match actual outcomes, accurately detecting changing areas’ spatial locations. This demonstrates that the edge-guided context aggregation proposed herein enhances the interaction between local detail and global semantic features during Transformer coding and decoding, improving detection efficacy. Compared with seven classical change detection methods on two datasets, our method outperforms the selected comparison methods. Ablation studies on the CLCD dataset further confirm the effectiveness of each module in enhancing overall performance.

Addressing boundary discontinuity and misdetection issues in land cover change detection of high-resolution remote sensing images, we design an adaptive boundary sensing method, which adopts a hybrid structure of CNN and Transformer. Selecting res2net as the encoder for multiscale feature extraction and differential enhancement, and leveraging edge features to guide Transformer for contextual information aggregation, we adopt a multi-scale output fusion strategy to combine global semantic and local detail features across layers. This approach yields more precise change detection results compared to other traditional methods, enhancing the model’s resilience to external condition interferences.