Satellite imagery

Satellite images (also Earth observation imagery, spaceborne photography, or simply satellite photo) are s of Earth collected by imaging satellites operated by governments and businesses around the world. Satellite imaging companies sell images by licensing them to governments and businesses such as Apple Maps and Google Maps. History The first images from space were taken on sub-orbital flights. The U.S-launched V-2 flight on October 24, 1946, took one image every 1.5 seconds. With an apogee of 65 miles (105 km), these photos were from five times higher than the previous record, the 13.7 miles (22 km) by the Explorer II balloon mission in 1935. The first satellite (orbital) photographs of Earth were made on August 14, 1959, by the U.S. Explorer 6. The first satellite photographs of the Moon might have been made on October 6, 1959, by the Soviet satellite Luna 3, on a mission to photograph the far side of the Moon. The Blue Marble photograph was taken from spa

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ENV-540: Image processing for Earth observation

This course covers optical remote sensing from satellites and airborne platforms. The different systems are presented. The students will acquire skills in image processing and machine/deep learning to extract end-products, such as land cover or risk maps, from the images.

ENV-341: Remote sensing

Ce cours a pour objectif de familiariser les étudiants avec les principaux concepts, instruments et techniques de la télédétection environnementale. Les interactions ondes/matière, les différents types instruments et les techniques de traitements d'images sont abordés.

CS-442: Computer vision

Computer Vision aims at modeling the world from digital images acquired using video or infrared cameras, and other imaging sensors. We will focus on images acquired using digital cameras. We will introduce basic processing techniques and discuss their field of applicability.

Classification of high resolution satellite images

In this thesis the Support Vector Machine (SVM)is applied on classification of high resolution satellite images. Sveral different measures for classification, including texture mesasures, 1st order statistics, and simple contextual information were evaluated. Additionnally, the image was segmented, using an enhanced watershed method, in order to improve the classification accuracy.

2003

Land cover classification of the semi-arid Namibian savanna

Beatrice Bacchilega

The vegetation of semi-arid savanna landscapes relies on the fragile equilibrium between rainfall, grazing and res [Trodd and Dougill, 1998]. For a sustainable management of their farms, farmers need to take into account this delicate equilibrium and correctly balance the amount of animals and the available food and water resources. These two quantities are estimated every year at the end of the growing season (end of May) to dene the yearly management plans [Reinhard, 2017]. Ground based techniques for animal counting and biomass estimation are long and time consuming processes that can hardly provide a good coverage of the farm [Reinhard, 2017]. Farmers are now more and more looking towards remote sensing techniques to overcome the eld work and achieve both these tasks. The main goal of this project was to classify the soil cover in Kuzikus Wildlife Reserve, in the north-eastern Kalahari region of Namibia, in order to provide an estimate of the food availability for wildlife at the end of the growing season. Ultra-high resolution UAV images and coarse resolution satellite images were classied using both supervised and unsupervised classication techniques. Random Forest and Support Vector Machine supervised classiers gave encouraging results and the research showed the great potential of UAV and satellite imagery for biomass estimation. Used together, these two complementary types of data allow to have both a detailed plant species recognition and a general overview of the study area.

2018

Land cover classification of the semi-arid Namibian savanna

Beatrice Bacchilega

The vegetation of semi-arid savanna landscapes relies on the fragile equilibrium between rainfall, grazing and fires . For a sustainable management of their farms, farmers need to take into account this delicate equilibrium and correctly balance the amount of animals and the available food and water resources. These two quantities are estimated every year at the end of the growing season (end of May) to define the yearly management plans. Ground based techniques for animal counting and biomass estimation are long and time consuming processes that can hardly provide a good coverage of the farm. Farmers are now more and more looking towards remote sensing techniques to overcome the field work and achieve both these tasks. The main goal of this project was to classify the soil cover in Kuzikus Wildlife Reserve, in the north-eastern Kalahari region of Namibia, in order to provide an estimate of the food availability for wildlife at the end of the growing season. Ultra-high resolution UAV images and coarse resolution satellite images were classified using both supervised and unsupervised classification techniques. Random Forest and Support Vector Machine supervised classifiers gave encouraging results and the research showed the great potential of UAV and satellite imagery for biomass estimation. Used together, these two complementary types of data allow to have both a detailed plant species recognition and a general overview of the study area.

2018

Remote sensing

Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object, in contrast to in situ or on-site observation. The term is applied esp

Geographic information system

A geographic information system (GIS) consists of integrated computer hardware and software that store, manage, analyze, edit, output, and visualize geographic data. Much of this often happens with

Aerial photography

Aerial photography (or airborne imagery) is the taking of photographs from an aircraft or other airborne platforms. When taking motion pictures, it is also known as aerial videography. Platforms