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

arXiv: https://arxiv.org/abs/2603.01498 · HTML: https://mlsanity.com/paper/2603.01498/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://mlsanity.com/paper/2306.14289/) (2023)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2309.01429/) (2023)
- [Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery](https://mlsanity.com/paper/1803.02642/) (2018)
- [Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2108.06103/) (2021)
- [Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2212.05245/) (2022)
- [Segment Any Change](https://mlsanity.com/paper/2402.01188/) (2024)
- [RSCaMa: Remote Sensing Image Change Captioning with State Space Model](https://mlsanity.com/paper/2404.18895/) (2024)
- [A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges](https://mlsanity.com/paper/2502.02835/) (2025)
- [DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning](https://mlsanity.com/paper/2406.13606/) (2024)
- [CDXLSTM: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory](https://mlsanity.com/paper/2411.07863/) (2024)
- [Changes in Gaza: DINOv3-Powered Multi-Class Change Detection for Damage Assessment in Conflict Zones](https://mlsanity.com/paper/2511.19035/) (2025)

## Abstract (arXiv)

In remote sensing imagery, multi-class change detection (MCD) is crucial for fine-grained monitoring, yet it has long been plagued by feature ambiguity in change regions, which obscures subtle changes and hinders fine-grained discrimination under complex scene variations and scarce annotations. To address this, we propose the Tri-path DINO architecture, which adopts a multi-path decoupled complementary feature learning strategy to separately capture coarse-grained semantic features and fine-grained target-edge features. 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 state-of-the-art performance on Gaza-change, SECOND, and LandsatSCD, improving mIoU/Fscd by 1.01%/2.93%, 0.67%/2.84%, and 1.22%/1.51%, respectively, over the best competing method. Grad-CAM 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.