# Task-Related Self-Supervised Learning for Remote Sensing Image Change Detection

arXiv: https://arxiv.org/abs/2105.04951 · HTML: https://mlsanity.com/paper/2105.04951/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 2 of 2)

- [Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery](https://mlsanity.com/paper/1803.02642/) (2018)
- [Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images](https://mlsanity.com/paper/1812.00645/) (2018)

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

- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [Cross Branch Feature Fusion Decoder for Consistency Regularization-based Semi-Supervised Change Detection](https://mlsanity.com/paper/2409.15021/) (2024)
- [Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model](https://mlsanity.com/paper/2412.11075/) (2024)
- [AquaCluster: Using Satellite Images And Self-supervised Machine Learning Networks To Detect Water Hidden Under Vegetation](https://mlsanity.com/paper/2506.08214/) (2025)

## Abstract (arXiv)

Change detection for remote sensing images is widely applied for urban change detection, disaster assessment and other fields. However, most of the existing CNN-based change detection methods still suffer from the problem of inadequate pseudo-changes suppression and insufficient feature representation. In this work, an unsupervised change detection method based on Task-related Self-supervised Learning Change Detection network with smooth mechanism(TSLCD) is proposed to eliminate it. The main contributions include: (1) the task-related self-supervised learning module is introduced to extract spatial features more effectively. (2) a hard-sample-mining loss function is applied to pay more attention to the hard-to-classify samples. (3) a smooth mechanism is utilized to remove some of pseudo-changes and noise. Experiments on four remote sensing change detection datasets reveal that the proposed TSLCD method achieves the state-of-the-art for change detection task.