# SAR Image Change Detection Based on Multiscale Capsule Network

arXiv: https://arxiv.org/abs/2201.08935 · HTML: https://mlsanity.com/paper/2201.08935/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection](https://mlsanity.com/paper/1905.01662/) (2019)

## Built on by (most cited first, 3 of 3)

- [Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network](https://mlsanity.com/paper/2203.06543/) (2022)
- [Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network](https://mlsanity.com/paper/2208.04481/) (2022)
- [Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network](https://mlsanity.com/paper/2605.16764/) (2026)

## Abstract (arXiv)

Traditional synthetic aperture radar image change detection methods based on convolutional neural networks (CNNs) face the challenges of speckle noise and deformation sensitivity. To mitigate these issues, we proposed a Multiscale Capsule Network (Ms-CapsNet) to extract the discriminative information between the changed and unchanged pixels. On the one hand, the multiscale capsule module is employed to exploit the spatial relationship of features. Therefore, equivariant properties can be achieved by aggregating the features from different positions. On the other hand, an adaptive fusion convolution (AFC) module is designed for the proposed Ms-CapsNet. Higher semantic features can be captured for the primary capsules. Feature extracted by the AFC module significantly improves the robustness to speckle noise. The effectiveness of the proposed Ms-CapsNet is verified on three real SAR datasets. The comparison experiments with four state-of-the-art methods demonstrate the efficiency of the proposed method. Our codes are available at https://github.com/summitgao/SAR_CD_MS_CapsNet .