# SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection

arXiv: https://arxiv.org/abs/2212.01287 · HTML: https://mlsanity.com/paper/2212.01287/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [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)
- [Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model](https://mlsanity.com/paper/1909.07726/) (2019)
- [DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images](https://mlsanity.com/paper/2003.03608/) (2020)
- [Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks](https://mlsanity.com/paper/1910.06444/) (2019)
- [RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder](https://mlsanity.com/paper/2010.15831/) (2020)

## Built on by (most cited first, 13 of 13)

- [Towards Generalizable Scene Change Detection](https://mlsanity.com/paper/2409.06214/) (2024)
- [Rethinking Remote Sensing Change Detection With A Mask View](https://mlsanity.com/paper/2406.15320/) (2024)
- [CDXLSTM: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory](https://mlsanity.com/paper/2411.07863/) (2024)
- [Viewpoint Integration and Registration with Vision Language Foundation Model for Image Change Understanding](https://mlsanity.com/paper/2309.08585/) (2023)
- [DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints](https://mlsanity.com/paper/2605.27957/) (2026)
- [Changes in Gaza: DINOv3-Powered Multi-Class Change Detection for Damage Assessment in Conflict Zones](https://mlsanity.com/paper/2511.19035/) (2025)
- [SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection](https://mlsanity.com/paper/2404.11326/) (2024)
- [Text Change Detection in Multilingual Documents Using Image Comparison](https://mlsanity.com/paper/2412.04137/) (2024)
- [UAV as Urban Construction Change Monitor: A New Benchmark and Change Captioning Model](https://mlsanity.com/paper/2605.04409/) (2026)
- [Bi-temporal Gaussian Feature Dependency Guided Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2410.09539/) (2024)
- [GRAD-Former: Gated Robust Attention-based Differential Transformer for Change Detection](https://mlsanity.com/paper/2603.01161/) (2026)
- [PhyUnfold-Net: Advancing Remote Sensing Change Detection with Physics-Guided Deep Unfolding](https://mlsanity.com/paper/2603.19566/) (2026)
- [AdaDINO: Pair-Aware In-Backbone Adaptation of Frozen DINO for Efficient Remote Sensing Change Detection](https://mlsanity.com/paper/2608.07982/) (2026)

## Abstract (arXiv)

Change detection (CD) aims to find the difference between two images at different times and outputs a change map to represent whether the region has changed or not. To achieve a better result in generating the change map, many State-of-The-Art (SoTA) methods design a deep learning model that has a powerful discriminative ability. However, these methods still get lower performance because they ignore spatial information and scaling changes between objects, giving rise to blurry or wrong boundaries. In addition to these, they also neglect the interactive information of two different images. To alleviate these problems, we propose our network, the Scale and Relation-Aware Siamese Network (SARAS-Net) to deal with this issue. In this paper, three modules are proposed that include relation-aware, scale-aware, and cross-transformer to tackle the problem of scene change detection more effectively. To verify our model, we tested three public datasets, including LEVIR-CD, WHU-CD, and DSFIN, and obtained SoTA accuracy. Our code is available at https://github.com/f64051041/SARAS-Net.