# DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning

arXiv: https://arxiv.org/abs/2406.13606 · HTML: https://mlsanity.com/paper/2406.13606/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [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)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [FcaNet: Frequency Channel Attention Networks](https://mlsanity.com/paper/2012.11879/) (2020)
- [Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model](https://mlsanity.com/paper/1909.07726/) (2019)
- [DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images](https://mlsanity.com/paper/2003.03608/) (2020)
- [Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2108.07002/) (2021)
- [Head-Free Lightweight Semantic Segmentation with Linear Transformer](https://mlsanity.com/paper/2301.04648/) (2023)

## Built on by (most cited first, 6 of 6)

- [Changes in Gaza: DINOv3-Powered Multi-Class Change Detection for Damage Assessment in Conflict Zones](https://mlsanity.com/paper/2511.19035/) (2025)
- [Consistency Change Detection Framework for Unsupervised Remote Sensing Change Detection](https://mlsanity.com/paper/2511.08904/) (2025)
- [A Spatial Semantics and Continuity Perception Attention for Remote Sensing Water Body Change Detection](https://mlsanity.com/paper/2511.16143/) (2025)
- [Beyond Segmentation: An Oil Spill Change Detection Framework Using Synthetic SAR Imagery](https://mlsanity.com/paper/2601.02139/) (2026)
- [Tri-path DINO: Feature Complementary Learning for Remote Sensing Multi-Class Change Detection](https://mlsanity.com/paper/2603.01498/) (2026)
- [Masks Can Talk: Extracting Structured Text Information from Single-Modal Images for Remote Sensing Change Detection](https://mlsanity.com/paper/2605.07178/) (2026)

## Abstract (arXiv)

Remote sensing change detection (RSCD) aims to identify the changes of interest in a region by analyzing multi-temporal remote sensing images, and has an outstanding value for local development monitoring. Existing RSCD methods are devoted to contextual modeling in the spatial domain to enhance the changes of interest. Despite the satisfactory performance achieved, the lack of knowledge in the frequency domain limits the further improvement of model performance. In this paper, we propose DDLNet, a RSCD network based on dual-domain learning (i.e., frequency and spatial domains). In particular, we design a Frequency-domain Enhancement Module (FEM) to capture frequency components from the input bi-temporal images using Discrete Cosine Transform (DCT) and thus enhance the changes of interest. Besides, we devise a Spatial-domain Recovery Module (SRM) to fuse spatiotemporal features for reconstructing spatial details of change representations. Extensive experiments on three benchmark RSCD datasets demonstrate that the proposed method achieves state-of-the-art performance and reaches a more satisfactory accuracy-efficiency trade-off. Our code is publicly available at https://github.com/xwmaxwma/rschange.