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Found 9 papers, 2 papers with code
CATSNet: a context-aware network for Height Estimation in a Forested Area based on Pol-TomoSAR data
Tropical forests are a key component of the global carbon cycle.
Pansharpening by convolutional neural networks in the full resolution framework
A further problem is the scarcity of training data, which causes a limited generalization ability and a poor performance on off-training test images.
Multi-Objective CNN Based Algorithm for SAR Despeckling
In this paper, a convolutional neural network (CNN) with a multi-objective cost function taking care of spatial and statistical properties of the SAR image is proposed.
Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm
Based on our last proposed convolutional neural network for SAR despeckling, here we exploit the effect of the complexity of the network.
A Novel Cost Function for Despeckling using Convolutional Neural Networks
Removing speckle noise from SAR images is still an open issue.
A New Ratio Image Based CNN Algorithm For SAR Despeckling
The aim is two fold: overcome the trade-off between speckle suppression and details suppression; find a suitable cost function for despeckling in unsupervised learning.
Guided patch-wise nonlocal SAR despeckling
We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery.
Target-adaptive CNN-based pansharpening
We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art.
