Change detection for remote sensing images
22 papers with code • 2 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Change detection for remote sensing images
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Libraries
Use these libraries to find Change detection for remote sensing images models and implementations
CDD Dataset (season-varying)
SECOND
ChaBuD
GVLM
Latest papers
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks.
Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images
SUST-reynole/USSFC-Net
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IEEE Transactions on Geoscience and Remote Sensing 2023
First, the existing multiscale feature fusion methods often use redundant feature extraction and fusion strategies, which often lead to high computational costs and memory usage.
Transition Is a Process: Pair-to-Video Change Detection Networks for Very High Resolution Remote Sensing Images
PaddlePaddle/PaddleRS
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IEEE Transactions on Image Processing 2022
In view of these issues, we propose a more explicit and sophisticated modeling of time and accordingly establish a pair-to-video change detection (P2V-CD) framework.
SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection
Change detection (CD) aims to find the difference between two images at different times and outputs a change map to represent whether the region has changed or not.
TINYCD: A (Not So) Deep Learning Model For Change Detection
likyoo/open-cd
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Despite being from 13 to 140 times smaller than the compared change detection models, and exposing at least a third of the computational complexity, our model outperforms the current state-of-the-art models by at least $1\%$ on both F1 score and IoU on the LEVIR-CD dataset, and more than $8\%$ on the WHU-CD dataset.
An Empirical Study of Remote Sensing Pretraining
To this end, we train different networks from scratch with the help of the largest RS scene recognition dataset up to now -- MillionAID, to obtain a series of RS pretrained backbones, including both convolutional neural networks (CNN) and vision transformers such as Swin and ViTAE, which have shown promising performance on computer vision tasks.
An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection
In this paper, we propose an end-to-end Supervised Domain Adaptation framework for cross-domain Change Detection, namely SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions.
Change Detection in VHR Imagery With Severe Co-Registration Errors Using Deep Learning: A Comparative Study
vkristoll/change-detection-autoencoder
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IEEE Access 2022
Based on that, the goal of this study is to evaluate the performance of five state-of-the-art DL CD methods, two unsupervised and three supervised, on VHR images with severe co-registration errors.
RDP-Net: Region Detail Preserving Network for Change Detection
chnja/rdpnet
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Moreover, current CNN models are heavy in parameters, which prevents their deployment on edge devices such as UAVs.
DSAMNet: A Deeply Supervised Attention Metric Based Network for Change Detection of High-Resolution Images
liumency/DSAMNet
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IEEE International Geoscience and Remote Sensing Symposium IGARSS 2021
In view of the insufficient of current change detection, we propose a deeply-supervised attention metric-based network (DSAMNet) for bi-temporal image change detection.
