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Found 5 papers, 2 papers with code
Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal
Removing the effect of illumination variation in images has been proved to be beneficial in many computer vision applications such as object recognition and semantic segmentation.
Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery via Filtered Jaccard Loss Function and Parametric Augmentation
Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis.
Ranked #1 on
Semantic Segmentation
on 38-Cloud
Cloud-Net: An end-to-end Cloud Detection Algorithm for Landsat 8 Imagery
Cloud detection in satellite images is an important first-step in many remote sensing applications.
Ranked #2 on
Semantic Segmentation
on 38-Cloud
Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks
This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images.
CPNet: A Context Preserver Convolutional Neural Network for Detecting Shadows in Single RGB Images
Automatic detection of shadow regions in an image is a difficult task due to the lack of prior information about the illumination source and the dynamic of the scene objects.
