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Found 9 papers, 1 papers with code
Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation
High resolution remote sensing imagery is used in broad range of tasks, including detection and classification of objects.
Convolutional Neural Networks for Multispectral Image Cloud Masking
Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks and their use is rapidly increasing in remote sensing problems.
Cloud detection machine learning algorithms for PROBA-V
This paper presents the development and implementation of a cloud detection algorithm for Proba-V.
Pattern Recognition Scheme for Large-Scale Cloud Detection over Landmarks
Landmark recognition and matching is a critical step in many Image Navigation and Registration (INR) models for geostationary satellite services, as well as to maintain the geometric quality assessment (GQA) in the instrument data processing chain of Earth observation satellites.
Randomized kernels for large scale Earth observation applications
Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time.
Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations
Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.}
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.
Fair Kernel Learning
It has been shown that not including sensitive features that bias fairness, such as gender or race, is not enough to mitigate the discrimination when other related features are included.
Optimized Kernel Entropy Components
Results show that 1) OKECA returns projections with more expressive power than KECA, 2) the most successful rule for estimating the kernel parameter is based on maximum likelihood, and 3) OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.
