Search results for "Cloud Masking"
showing 3 items of 3 documents
Transfer Learning of Deep Learning Models for Cloud Masking in Optical Satellite Images
2023
Los satélites de observación de la Tierra proporcionan una oportunidad sin precedentes para monitorizar nuestro planeta a alta resolución tanto espacial como temporal. Sin embargo, para procesar toda esta cantidad creciente de datos, necesitamos desarrollar modelos rápidos y precisos adaptados a las características específicas de los datos de cada sensor. Para los sensores ópticos, detectar las nubes en la imagen es un primer paso inevitable en la mayoría de aplicaciones tanto terrestres como oceánicas. Aunque detectar nubes brillantes y opacas es relativamente fácil, identificar automáticamente nubes delgadas semitransparentes o diferenciar nubes de nieve o superficies brillantes es mucho …
Robustified smoothing for enhancement of thermal image sequences affected by clouds
2015
Obtaining radiometric surface temperature information with both high acquisition rate and high spatial resolution is still not possible through a single sensor. However, in several earth observation applications, the fusion of data acquired by different sensors is a viable solution for so called image sharpening. A related issue is the presence of clouds, which may impair the performance of the data fusion algorithms. In this paper we propose a robustified setup for the sharpening of thermal images in a non real-time scenario, capable to deal with missing thermal data due to cloudy pixels, and robust with respect to cloud mask misclassifications. The effectiveness of the presented technique…
Multitemporal Cloud Masking in the Google Earth Engine
2018
The exploitation of Earth observation satellite images acquired by optical instruments requires an automatic and accurate cloud detection. Multitemporal approaches to cloud detection are usually more powerful than their single scene counterparts since the presence of clouds varies greatly from one acquisition to another whereas surface can be assumed stationary in a broad sense. However, two practical limitations usually hamper their operational use: the access to the complete satellite image archive and the required computational power. This work presents a cloud detection and removal methodology implemented in the Google Earth Engine (GEE) cloud computing platform in order to meet these r…