Change Detection
185 papers with code • 8 benchmarks • 9 datasets
Change Detection is a computer vision task that involves detecting changes in an image or video sequence over time. The goal is to identify areas in the image or video that have undergone changes, such as appearance changes, object disappearance or appearance, or even changes in the scene's background.
Image credit: "A TRANSFORMER-BASED SIAMESE NETWORK FOR CHANGE DETECTION"
Benchmarks
These leaderboards are used to track progress in Change Detection
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Libraries
Use these libraries to find Change Detection models and implementations
LEVIR-CD
S2Looking
ChangeSim
V-PCCD
ChaBuD
Most implemented papers
TristouNet: Triplet Loss for Speaker Turn Embedding
TristouNet is a neural network architecture based on Long Short-Term Memory recurrent networks, meant to project speech sequences into a fixed-dimensional euclidean space.
A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection
justchenhao/STANet
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Remote Sensing 2020
Remote sensing image change detection (CD) is done to identify desired significant changes between bitemporal images.
SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images
Recent change detection methods always focus on the extraction of deep change semantic feature, but ignore the importance of shallow-layer information containing high-resolution and fine-grained features, this often leads to the uncertainty of the pixels at the edge of the changed target and the determination miss of small targets.
Remote Sensing Image Change Detection with Transformers
To achieve this, we express the bitemporal image into a few tokens, and use a transformer encoder to model contexts in the compact token-based space-time.
Fully Convolutional Siamese Networks for Change Detection
rcdaudt/fully_convolutional_change_detection
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This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images.
Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
\begin{abstract} The advent of multitemporal high resolution data, like the Copernicus Sentinel-2, has enhanced significantly the potential of monitoring the earth's surface and environmental dynamics.
xBD: A Dataset for Assessing Building Damage from Satellite Imagery
DIUx-xView/xview2-baseline
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xBD is the largest building damage assessment dataset to date, containing 850, 736 building annotations across 45, 362 km\textsuperscript{2} of imagery.
A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images
Then, the extracted deep features are fed into a deeply supervised difference discrimination network (DDN) for change detection.
Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networks
I-Hope-Peace/ChangeDetectionRepository
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Based on the unit two novel deep siamese convolutional neural networks, called as deep siamese multi-scale convolutional network (DSMS-CN) and deep siamese multi-scale fully convolutional network (DSMS-FCN), are designed for unsupervised and supervised change detection, respectively.
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
vincent-leguen/STDL
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NeurIPS 2019
We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.
