# Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection

arXiv: https://arxiv.org/abs/1904.08208 · HTML: https://mlsanity.com/paper/1904.08208/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [Guided Depth Super-Resolution by Deep Anisotropic Diffusion](https://mlsanity.com/paper/2211.11592/) (2022)
- [Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2204.08454/) (2022)
- [Self-supervised pre-training enhances change detection in Sentinel-2 imagery](https://mlsanity.com/paper/2101.08122/) (2021)
- [DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing](https://mlsanity.com/paper/2201.01047/) (2022)
- [Weakly Supervised Change Detection Using Guided Anisotropic Difusion](https://mlsanity.com/paper/2112.15367/) (2021)
- [Diffusion-RSCC: Diffusion Probabilistic Model for Change Captioning in Remote Sensing Images](https://mlsanity.com/paper/2405.12875/) (2024)
- [SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images](https://mlsanity.com/paper/2303.04737/) (2023)
- [Mapping Vulnerable Populations with AI](https://mlsanity.com/paper/2107.14123/) (2021)
- [Four decades of circumpolar super-resolved satellite land surface temperature data](https://mlsanity.com/paper/2511.17134/) (2025)
- [Content-Induced Spatial-Spectral Aggregation Network for Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2606.10328/) (2026)

## Abstract (arXiv)

Large scale datasets created from user labels or openly available data have become crucial to provide training data for large scale learning algorithms. While these datasets are easier to acquire, the data are frequently noisy and unreliable, which is motivating research on weakly supervised learning techniques. In this paper we propose an iterative learning method that extracts the useful information from a large scale change detection dataset generated from open vector data to train a fully convolutional network which surpasses the performance obtained by naive supervised learning. We also propose the guided anisotropic diffusion algorithm, which improves semantic segmentation results using the input images as guides to perform edge preserving filtering, and is used in conjunction with the iterative training method to improve results.