# Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networks

arXiv: https://arxiv.org/abs/1906.11479 · HTML: https://mlsanity.com/paper/1906.11479/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model](https://mlsanity.com/paper/2404.03425/) (2024)
- [Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks](https://mlsanity.com/paper/2112.01715/) (2021)
- [Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning](https://mlsanity.com/paper/2210.00941/) (2022)
- [LSNet: Extremely Light-Weight Siamese Network For Change Detection in Remote Sensing Image](https://mlsanity.com/paper/2201.09156/) (2022)
- [Change detection needs change information: improving deep 3D point cloud change detection](https://mlsanity.com/paper/2304.12639/) (2023)
- [Mamba-FCS: Joint Spatio- Frequency Feature Fusion, Change-Guided Attention, and SeK Loss for Enhanced Semantic Change Detection in Remote Sensing](https://mlsanity.com/paper/2508.08232/) (2025)
- [Towards Deep and Efficient: A Deep Siamese Self-Attention Fully Efficient Convolutional Network for Change Detection in VHR Images](https://mlsanity.com/paper/2108.08157/) (2021)
- [VSCD: Video-based Scene Change Detection in Unaligned Scenes](https://mlsanity.com/paper/2605.20821/) (2026)
- [UrbanCDNet: Appearance-Robust and Boundary-Aware Bitemporal Change Detection for Korean Urban Building Monitoring](https://mlsanity.com/paper/2606.29781/) (2026)

## Abstract (arXiv)

Very-high-resolution (VHR) images can provide abundant ground details and spatial geometric information. Change detection in multi-temporal VHR images plays a significant role in urban expansion and area internal change analysis. Nevertheless, traditional change detection methods can neither take full advantage of spatial context information nor cope with the complex internal heterogeneity of VHR images. In this paper, a powerful feature extraction model entitled multi-scale feature convolution unit (MFCU) is adopted for change detection in multi-temporal VHR images. MFCU can extract multi-scale spatial-spectral features in the same layer. Based on the unit two novel deep siamese convolutional neural networks, called as deep siamese multi-scale convolutional network (DSMS-CN) and deep siamese multi-scale fully convolutional network (DSMS-FCN), are designed for unsupervised and supervised change detection, respectively. For unsupervised change detection, an automatic pre-classification is implemented to obtain reliable training samples, then DSMS-CN fits the statistical distribution of changed and unchanged areas from selected training samples through MFCU modules and deep siamese architecture. For supervised change detection, the end-to-end deep fully convolutional network DSMS-FCN is trained in any size of multi-temporal VHR images, and directly outputs the binary change map. In addition, for the purpose of solving the inaccurate localization problem, the fully connected conditional random field (FC-CRF) is combined with DSMS-FCN to refine the results. The experimental results with challenging data sets confirm that the two proposed architectures perform better than the state-of-the-art methods.