Cloud Detection
20 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Cloud Detection
Sentinel 2 manually extracted deep water spectra with high noise levels and sunglint
Most implemented papers
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.
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.
Multi-label Cloud Segmentation Using a Deep Network
Different empirical models have been developed for cloud detection.
CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation
In the existing literature, however, analysis of daytime and nighttime images is considered separately, mainly because of differences in image characteristics and applications.
A framework for deep learning emulation of numerical models with a case study in satellite remote sensing
A difficult test for deep learning-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner.
DeepMask: an algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network
The average accuracy is 93. 56%, compared with 85. 36% from CFMask.
Artificial neural networks for cloud masking of Sentinel-2 ocean images with noise and sunglint
It was shown that the ANNs trained on the second dataset perform very favourably, in contrast to the ANNs trained on the first dataset that fails to adequately represent the spectra of the noisy Sentinel-2 images.
Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection
In addition, the training of the proposed adversarial domain adaptation model can be modified to improve the performance in a specific remote sensing application, such as cloud detection, by including a dedicated term in the cost function.
Fine-Tuning Self-Organizing Maps for Sentinel-2 Imagery: Separating Clouds from Bright Surfaces
This study evaluated a SOM for cloud masking Sentinel-2 images and proposed a fine-tuning methodology to separate clouds from bright land areas.
Convolutional neural networks for detecting challenging cases in cloud masking using Sentinel-2 imagery
Cloud contamination represents a large obstacle for mapping the earth’s surface using remotely sensed data.
