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Found 10 papers, 4 papers with code
C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images
A high-precision feature extraction model is crucial for change detection (CD).
Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery
The rapid advancement of automated artificial intelligence algorithms and remote sensing instruments has benefited change detection (CD) tasks.
Ranked #1 on
Change Detection
on LEVIR+
HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images
Benefiting from the developments in deep learning technology, deep-learning-based algorithms employing automatic feature extraction have achieved remarkable performance on the change detection (CD) task.
Ranked #1 on
Change Detection
on GoogleGZ-CD
GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection
High spectral resolution imagery of the Earth's surface enables users to monitor changes over time in fine-grained scale, playing an increasingly important role in agriculture, defense, and emergency response.
EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection
Hyperspectral change detection plays an essential role of monitoring the dynamic urban development and detecting precise fine object evolution and alteration.
HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection
Only the positive samples at the same location of bi-temporal HSIs are created and forced to be aligned, aiming at learning the spectral difference-invariant features.
Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection
Hyperspectral anomalous change detection has been a challenging task for its emphasis on the dynamics of small and rare objects against the prevalent changes.
Binary Change Guided Hyperspectral Multiclass Change Detection
In this study, we proposed an unsupervised Binary Change Guided hyperspectral multiclass change detection Network (BCG-Net) for HMCD, which aims at boosting the multiclass change detection result and unmixing result with the mature binary change detection approaches.
Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery
Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.
Hyperspectral Anomaly Change Detection Based on Auto-encoder
In the ACDA model, two systematic auto-encoder (AE) networks are deployed to construct two predictors from two directions.
