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Found 7 papers, 3 papers with code
Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping
These differences are stacked with the original pre- and post-event images and passed to an OCC trained on a small sample from the targeted change class.
Power Flow Balancing with Decentralized Graph Neural Networks
We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids.
On the potential of sequential and non-sequential regression models for Sentinel-1-based biomass prediction in Tanzanian miombo forests
We develop a traditional, parametric regression model and alternative non-parametric models for this stage.
Guided Nonlocal Means Estimation of Polarimetric Covariance for Canopy State Classification
We have developed a nonlocal algorithm for estimating polarimetric synthetic aperture radar (PolSAR) covariance matrices on single-look complex (SLC) format resolution.
Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping
In this study we investigate the potential for using synthetic aperture radar (SAR) data to provide high resolution defoliation and regrowth mapping of trees in the tundra-forest ecotone.
Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection.
A clustering approach to heterogeneous change detection
Change detection in heterogeneous multitemporal satellite images is a challenging and still not much studied topic in remote sensing and earth observation.
