# Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network

arXiv: https://arxiv.org/abs/2203.06543 · HTML: https://mlsanity.com/paper/2203.06543/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 62 references that are in the map.
- First posted on arXiv 13 Mar 2022.

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

- [Hyperspectral Image Classification in the Presence of Noisy Labels](https://mlsanity.com/paper/1809.04212/) (2018)
- [Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network](https://mlsanity.com/paper/2104.06699/) (2021)
- [SAR Image Change Detection Based on Multiscale Capsule Network](https://mlsanity.com/paper/2201.08935/) (2022)
- [Synthetic Aperture Radar Image Change Detection via Siamese Adaptive Fusion Network](https://mlsanity.com/paper/2110.09049/) (2021)

## Built on by (most cited first, 4 of 4)

- [Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing](https://mlsanity.com/paper/2310.12452/) (2023)
- [Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection](https://mlsanity.com/paper/2309.12010/) (2023)
- [Wavelet-based Bi-dimensional Aggregation Network for SAR Image Change Detection](https://mlsanity.com/paper/2407.13151/) (2024)
- [Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network](https://mlsanity.com/paper/2605.16764/) (2026)

## Abstract (arXiv)

Benefited from the rapid and sustainable development of synthetic aperture radar (SAR) sensors, change detection from SAR images has received increasing attentions over the past few years. Existing unsupervised deep learning-based methods have made great efforts to exploit robust feature representations, but they consume much time to optimize parameters. Besides, these methods use clustering to obtain pseudo-labels for training, and the pseudo-labeled samples often involve errors, which can be considered as "label noise". To address these issues, we propose a Dual Path Denoising Network (DPDNet) for SAR image change detection. In particular, we introduce the random label propagation to clean the label noise involved in preclassification. We also propose the distinctive patch convolution for feature representation learning to reduce the time consumption. Specifically, the attention mechanism is used to select distinctive pixels in the feature maps, and patches around these pixels are selected as convolution kernels. Consequently, the DPDNet does not require a great number of training samples for parameter optimization, and its computational efficiency is greatly enhanced. Extensive experiments have been conducted on five SAR datasets to verify the proposed DPDNet. The experimental results demonstrate that our method outperforms several state-of-the-art methods in change detection results.