Search results for "SATELLITE IMAGES"

Exploiting deep learning algorithms and satellite image time series for deforestation prediction

2022

In recent years, we have witnessed the emergence of Deep Learning (DL) methods, which have led to enormous progress in various fields such as automotive driving, computer vision, medicine, finances, and remote sensing data analysis. The success of these machine learning methods is due to the ever-increasing availability of large amounts of information and the computational power of computers. In the field of remote sensing, we now have considerable volumes of satellite images thanks to the large number of Earth Observation (EO) satellites orbiting the planet. With the revisit time of satellites over an area becoming shorter and shorter, it will probably soon be possible to obtain daily imag…

Relative risk estimation of dengue disease at small spatial scale

2017

Abstract Background Dengue is a high incidence arboviral disease in tropical countries around the world. Colombia is an endemic country due to the favourable environmental conditions for vector survival and spread. Dengue surveillance in Colombia is based in passive notification of cases, supporting monitoring, prediction, risk factor identification and intervention measures. Even though the surveillance network works adequately, disease mapping techniques currently developed and employed for many health problems are not widely applied. We select the Colombian city of Bucaramanga to apply Bayesian areal disease mapping models, testing the challenges and difficulties of the approach. Methods…

Using optical satellite and aerial imagery for automatic coastline mapping

2020

The continuous availability and rapid accessibility to multispectral data from satellite platforms within the Copernicus Programme represents a great opportunity for users in different fields of applications as: Agriculture, observation of coastal zones, monitoring land cover change. The aim of this paper is to identify a suitable method to map coastline using Sentinel-2 optical satellite image. The method provides the use of two indexes developed in remote sensing field for water environment: NDWI (Normalized difference water index) and MNDWI (Modified Normalized difference water index). Starting from the construction of maps of these indexes and, identifying appropriate threshold values, …

Methods and Techniques for Multi-source Data Analysis and Fusion

This work has been inspired by the recent trend in remote sensing and environmental data acquisition. Remote sensing techniques allow us to measure information about an object without touching it. In the last decades remote sensing via satellites has been used in various applications such as Earth observation, weather and storm predictive analysis, atmospheric monitoring, climate change, human-environment interactions. Sensors on airborne and satellite platforms have been recording signals from space for many years, giving rise to a huge amount of data. Some data are processed on-board but others are treated and post-processed in ground stations. Signal and image processing are widely appli…

Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Int…

2022

Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining…

Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series

2020

L'analyse prédictive permet d'estimer les tendances des évènements futurs. De nos jours, les algorithmes Deep Learning permettent de faire de bonnes prédictions. Cependant, pour chaque type de problème donné, il est nécessaire de choisir l'architecture optimale. Dans cet article, les modèles Stack-LSTM, CNN-LSTM et ConvLSTM sont appliqués à une série temporelle d'images radar sentinel-1, le but étant de prédire la prochaine occurrence dans une séquence. Les résultats expérimentaux évalués à l'aide des indicateurs de performance tels que le RMSE et le MAE, le temps de traitement et l'index de similarité SSIM, montrent que chacune des trois architectures peut produire de bons résultats en fon…

The Mistigri Thermal Infrared Project: Scientific Objectives And Mission Specifications

2013

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The Networked Utilisation of Satellite Images and Geospatial Technology in Journalism

2022

Satellite technology has become increasingly affordable and accessible in the past few decades. This has enabled many newsrooms to engage with genuinely remote journalism. This chapter explores the networked nature of satellite journalism and investigates its inclusion of experts and citizens outside newsrooms. Satellite journalism uses satellite-borne technology either as a (1) part of storytelling or (2) source of information. Academic research on the topic is scarce, so this chapter builds a base of knowledge on the contemporary practices, limitations and ethics—as well as directions of future development—of satellite journalism. By interviewing six journalists and one earth observation …