# FCCDN: Feature Constraint Network for VHR Image Change Detection

arXiv: https://arxiv.org/abs/2105.10860 · HTML: https://mlsanity.com/paper/2105.10860/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 72 references that are in the map.
- First posted on arXiv 23 May 2021.

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Using Pre-Training Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1901.09960/) (2019)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data](https://mlsanity.com/paper/1904.00592/) (2019)
- [Pyramid Attention Network for Semantic Segmentation](https://mlsanity.com/paper/1805.10180/) (2018)
- [OCNet: Object Context Network for Scene Parsing](https://mlsanity.com/paper/1809.00916/) (2018)
- [Learning to Compare Image Patches via Convolutional Neural Networks](https://mlsanity.com/paper/1504.03641/) (2015)
- [DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images](https://mlsanity.com/paper/2003.03608/) (2020)
- [Accurate Optical Flow via Direct Cost Volume Processing](https://mlsanity.com/paper/1704.07325/) (2017)
- [Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples](https://mlsanity.com/paper/2010.00882/) (2020)
- [The color out of space: learning self-supervised representations for Earth Observation imagery](https://mlsanity.com/paper/2006.12119/) (2020)
- [Self-supervised Change Detection in Multi-view Remote Sensing Images](https://mlsanity.com/paper/2103.05969/) (2021)
- [From W-Net to CDGAN: Bi-temporal Change Detection via Deep Learning Techniques](https://mlsanity.com/paper/2003.06583/) (2020)
- [Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data](https://mlsanity.com/paper/1910.07778/) (2019)
- [Looking for change? Roll the Dice and demand Attention](https://mlsanity.com/paper/2009.02062/) (2020)

## Built on by (most cited first, 25 of 25)

- [RS-Mamba for Large Remote Sensing Image Dense Prediction](https://mlsanity.com/paper/2404.02668/) (2024)
- [Changer: Feature Interaction is What You Need for Change Detection](https://mlsanity.com/paper/2209.08290/) (2022)
- [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022)
- [Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization](https://mlsanity.com/paper/2311.11302/) (2023)
- [Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange](https://mlsanity.com/paper/2310.00689/) (2023)
- [Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2302.05109/) (2023)
- [SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images](https://mlsanity.com/paper/2308.14570/) (2023)
- [RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model](https://mlsanity.com/paper/2403.07564/) (2024)
- [Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review](https://mlsanity.com/paper/2205.06407/) (2022)
- [SSLChange: A Self-supervised Change Detection Framework Based on Domain Adaptation](https://mlsanity.com/paper/2405.18224/) (2024)
- [PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection](https://mlsanity.com/paper/2509.09572/) (2025)
- [EfficientCD: A New Strategy For Change Detection Based With Bi-temporal Layers Exchanged](https://mlsanity.com/paper/2407.15999/) (2024)
- [RCDT: Relational Remote Sensing Change Detection with Transformer](https://mlsanity.com/paper/2212.04869/) (2022)
- [BCE-Net: Reliable Building Footprints Change Extraction based on Historical Map and Up-to-Date Images using Contrastive Learning](https://mlsanity.com/paper/2304.07076/) (2023)
- [MASNet:Improve Performance of Siamese Networks with Mutual-attention for Remote Sensing Change Detection Tasks](https://mlsanity.com/paper/2206.02331/) (2022)
- [SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective](https://mlsanity.com/paper/2503.20734/) (2025)
- [Lightweight Adapter Learning for More Generalized Remote Sensing Change Detection](https://mlsanity.com/paper/2504.19598/) (2025)
- [Towards Remote Sensing Change Detection with Neural Memory](https://mlsanity.com/paper/2602.10491/) (2026)
- [SwinV2DNet: Pyramid and Self-Supervision Compounded Feature Learning for Remote Sensing Images Change Detection](https://mlsanity.com/paper/2308.11159/) (2023)
- [Explicit Change Relation Learning for Change Detection in VHR Remote Sensing Images](https://mlsanity.com/paper/2311.07993/) (2023)
- [HSONet:A Siamese foreground association-driven hard case sample optimization network for high-resolution remote sensing image change detection](https://mlsanity.com/paper/2402.16242/) (2024)
- [Generalization-aware Remote Sensing Change Detection via Domain-agnostic Learning](https://mlsanity.com/paper/2504.00543/) (2025)
- [Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction](https://mlsanity.com/paper/2505.12280/) (2025)
- [DGL-RSIS: Decoupling Global Spatial Context and Local Class Semantics for Training-Free Remote Sensing Image Segmentation](https://mlsanity.com/paper/2509.00598/) (2025)
- [JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering](https://mlsanity.com/paper/2606.31745/) (2026)

## Abstract (arXiv)

Change detection is the process of identifying pixelwise differences in bitemporal co-registered images. It is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on two building change detection datasets (LEVIR-CD and WHU). On the LEVIR-CD dataset, we achieve an IoU of 0.8569 and an F1 score of 0.9229. On the WHU dataset, we achieve an IoU of 0.8820 and an F1 score of 0.9373. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling.