ChenHongruixuan/KPCAMNet

[IEEE TCYB 2022] Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

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Emerging

This tool helps analyze changes over time in satellite or aerial imagery without needing pre-labeled examples of what has changed. You provide two high-resolution images of the same area taken at different times, and it produces a map highlighting where and how the land or structures have changed. This is ideal for environmental scientists, urban planners, or disaster response teams monitoring landscapes.

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Use this if you need to automatically identify and classify changes in very-high-resolution satellite or aerial images, such as urban expansion or deforestation, and you don't have access to existing labeled training data.

Not ideal if your images are low resolution, if you need real-time change detection, or if you prefer to explicitly train a model with extensively labeled datasets.

remote-sensing earth-observation land-use-monitoring environmental-monitoring urban-planning
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 9 / 25
Maturity 8 / 25
Community 19 / 25

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Language

Python

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Last pushed

Jan 31, 2024

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