Tri-path DINO: Feature Complementary Learning for Remote Sensing Multi-Class Change Detection

TL;DR

Tri-path DINO introduces a three-path feature learning architecture that enhances multi-class change detection in remote sensing images by combining coarse and fine features, leading to improved accuracy and interpretability.

Contribution

The paper proposes the Tripath DINO architecture, integrating a pre-trained backbone, an auxiliary siamese path, and multi-scale attention for better feature learning in complex remote sensing scenes.

Findings

Achieves state-of-the-art performance on Gaza and SECOND datasets.

GradCAM visualizations show focus on semantic and structural changes.

Provides a robust, interpretable change detection method.

Abstract

In remote sensing imagery, multi class change detection (MCD) is crucial for fine grained monitoring, yet it has long been constrained by complex scene variations and the scarcity of detailed annotations. To address this, we propose the Tripath DINO architecture, which adopts a three path complementary feature learning strategy to facilitate the rapid adaptation of pre trained foundation models to complex vertical domains. Specifically, we employ the DINOv3 pre trained model as the backbone feature extraction network to learn coarse grained features. An auxiliary path also adopts a siamese structure, progressively aggregating intermediate features from the siamese encoder to enhance the learning of fine grained features. Finally, a multi scale attention mechanism is introduced to augment the decoder network, where parallel convolutions adaptively capture and enhance contextual information under different receptive fields. The proposed method achieves optimal performance on the MCD task on both the Gaza facility damage assessment dataset (Gaza change) and the classic SECOND dataset. GradCAM visualizations further confirm that the main and auxiliary paths naturally focus on coarse grained semantic changes and fine grained structural details, respectively. This synergistic complementarity provides a robust and interpretable solution for advanced change detection tasks, offering a basis for rapid and accurate damage assessment.