# A Dual Attentive Generative Adversarial Network for Remote Sensing Image Change Detection

arXiv: https://arxiv.org/abs/2310.01876 · HTML: https://mlsanity.com/paper/2310.01876/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 3 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions](https://mlsanity.com/paper/2007.01628/) (2020)
- [Semantic Segmentation using Adversarial Networks](https://mlsanity.com/paper/1611.08408/) (2016)
- [Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network](https://mlsanity.com/paper/1912.08628/) (2019)
- [High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Glioma Segmentation](https://mlsanity.com/paper/2006.05030/) (2020)
- [Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data](https://mlsanity.com/paper/2104.02837/) (2021)

## Built on by (most cited first, 1 of 1)

- [Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning](https://mlsanity.com/paper/2412.19179/) (2024)

## Abstract (arXiv)

Remote sensing change detection between bi-temporal images receives growing concentration from researchers. However, comparing two bi-temporal images for detecting changes is challenging, as they demonstrate different appearances. In this paper, we propose a dual attentive generative adversarial network for achieving very high-resolution remote sensing image change detection tasks, which regards the detection model as a generator and attains the optimal weights of the detection model without increasing the parameters of the detection model through generative-adversarial strategy, boosting the spatial contiguity of predictions. Moreover, We design a multi-level feature extractor for effectively fusing multi-level features, which adopts the pre-trained model to extract multi-level features from bi-temporal images and introduces aggregate connections to fuse them. To strengthen the identification of multi-scale objects, we propose a multi-scale adaptive fusion module to adaptively fuse multi-scale features through various receptive fields and design a context refinement module to explore contextual dependencies. Moreover, the DAGAN framework utilizes the 4-layer convolution network as a discriminator to identify whether the synthetic image is fake or real. Extensive experiments represent that the DAGAN framework has better performance with 85.01% mean IoU and 91.48% mean F1 score than advanced methods on the LEVIR dataset.