# Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network

arXiv: https://arxiv.org/abs/2208.04481 · HTML: https://mlsanity.com/paper/2208.04481/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network](https://mlsanity.com/paper/2104.06699/) (2021)
- [Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network](https://mlsanity.com/paper/2201.08954/) (2022)
- [SAR Image Change Detection Based on Multiscale Capsule Network](https://mlsanity.com/paper/2201.08935/) (2022)

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

- [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (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)
- [Collaborative Learning of Scattering and Deep Features for SAR Target Recognition with Noisy Labels](https://mlsanity.com/paper/2508.07656/) (2025)

## Abstract (arXiv)

Recently, change detection methods for synthetic aperture radar (SAR) images based on convolutional neural networks (CNN) have gained increasing research attention. However, existing CNN-based methods neglect the interactions among multilayer convolutions, and errors involved in the preclassification restrict the network optimization. To this end, we proposed a layer attention-based noise-tolerant network, termed LANTNet. In particular, we design a layer attention module that adaptively weights the feature of different convolution layers. In addition, we design a noise-tolerant loss function that effectively suppresses the impact of noisy labels. Therefore, the model is insensitive to noisy labels in the preclassification results. The experimental results on three SAR datasets show that the proposed LANTNet performs better compared to several state-of-the-art methods. The source codes are available at https://github.com/summitgao/LANTNet