UAV - based imagery processing using structure from motion and remote sensing technology

With the recent advances in technology and miniaturization of devices such as GPS or IMU, Unmanned Aerial Vehicles became a feasible platform for a Remote Sensing applications. The use of UAVs compared to the conventional aerial platforms provides a set of advantages such as higher spatial resolution of the derived products. UAV - based imagery obtained by a user grade cameras introduces a set of problems which have to be solved, e. g. rotational or angular differences or unknown or insufficiently precise IO and EO camera parameters. In this work, UAV - based imagery of RGB and CIR type was processed using two different workflows based on PhotoScan and VisualSfM software solutions resulting in the DSM and orthophoto products. Feature detection and matching parameters influence on the result quality as well as a processing time was examined and the optimal parameter setup was presented. Products of the both workflows were compared in terms of a quality and a spatial accuracy. Both workflows were compared by presenting the processing times and quality of the results. Finally, the obtained products were used in order to demonstrate vegetation classification. Contribution of the IHS transformations was examined with respect to the classification accuracy.

Viri, Remote execution of Python scripts

Viri is a system for automatic distribution and execution of Python code on remote machines. This is especially useful when dealing with a large group of hosts.With Viri, Sysadmins can write their own scripts, and easily distribute and execute them on any number of remote machines. Depending on the number of computers to administrate, Viri can save thousands of hours, that Sysadmins would spend transferring files, logging into remote hosts, and waiting for the scripts to finish. Viri automates the whole process.Viri can also be useful for remotely managing host settings. It should work together with an application where the information about hosts would be maintained. This information can include cron tasks, firewall rules, backup settings,... After a simple Integration of this application with your Viri infrastructure, you can change any settings in the application, and see how it gets applied on the target host automatically.

Parallelizing remote sensing image geometric correction

Remote sensing spatial, spectral, and temporal resolutions of images, acquired over a reasonably sized image extent, result in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is very attractive for monitoring, management, and scienti c activities. With Moore's Law alive and well, more and more parallelism is introduced into all computing platforms, at all levels of integration and programming to achieve higher performance and energy e ciency. Being the geometric calibration process one of the most time consuming processes when using remote sensing images, the aim of this work is to accelerate this process by taking advantage of new computing architectures and technologies, specially focusing in exploiting computation over shared memory multi-threading hardware. A parallel implementation of the most time consuming process in the remote sensing geometric correction has been implemented using OpenMP directives. This work compares the performance of the original serial binary versus the parallelized implementation, using several multi-threaded modern CPU architectures, discussing about the approach to nd the optimum hardware for a cost-e ective execution.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Multispectral remote sensing for site-specific nitrogen fertilizer management

The objective of this work was to evaluate the use of multispectral remote sensing for site-specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn-planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (Savi), optimized soil-adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R²=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0-2.5 kg), medium (2.5-3 kg), and high (3-3.3 kg).

Upgrading of thethird generation of the Remote Diagnostic Systems for ABB Marine Propulsion Systems

Remote diagnostics has become very popular in marine industry. Diagnostic systems are improved all the time and the newest monitoring systems are constantly taken into operation. Most vessels are, however, rigged up by outmoded facilities, which should be updated. In this work the principles of operating of such a remote diagnostic system as the ABB's third generation remote diagnostic system (RDS) and the newest Propulsion Condition Management System (PCMS) are studied. As a result of the thesis the ways of upgrading the old systems of RDS are presented. Specifically, the work focuses on the establishment of the connection between Advant controller AC-110 from the old system and PCMS server, and in other words how to get Modbus data by OPC server.

Transparent and secure remote network storage system using an untrusted server

Työssä kehitettin läpinäkyvä Internet Small Computer Systems Interface-verkkolevyä (iSCSI) käyttävä varmistusjärjestelmä. Verkkolevyn sisältö suojattiin asiakaspään salauskerroksella (dm-crypt). Järjestely mahdollisti sen, että verkkolevylle tallennetut varmuuskopiot pysyivät luottamuksellisina, vaikka levypalvelinta tarjoava taho oli joko epäluotettava tai suorastaan vihamielinen. Järjestelmän hyötykäyttöä varten kehitettiin helppokäyttöinen prototyyppisovellus. Järjestelmän riskit ja haavoittuvuudet käytiin läpi ja analysoitiin. Järjestelmälle tehtiin myös karkea kryptoanalyysi sen teknistenominaisuuksien pohjalta. Suorituskykymittaukset tehtiin sekä salatulle että salaamattomalle iSCSI-liikenteelle. Näistä todettiin, että salauksen vaikutus suorituskykyyn oli häviävän pieni jopa 100 megabittiä sekunnissa siirtävillä verkkonopeuksilla. Lisäksi pohdittiin teknologian muita sovelluskohteita ja tulevia tutkimusalueita.

Remote File Access with Wireless Symbian Devices

Erilaisten langattomien päätelaitteiden kuten älypuhelimien ja kommunikaattoreiden määrän lisääntyessä myös kiinnostus liikkuville käyttäjille lisäarvoa tuottavia verkkopalveluita ja -sovelluksia kohtaan kasvaa. Työn tarkoituksena oli tutkia kuinka langattomat Symbianin käyttöjärjestelmää käyttävät laitteet voivat hyödyntää verkkotiedostoja. Työssä arvioitiin eri tiedostojenjakoprotokollien käytettävyyttä langattomissa verkoissa, määriteltiin etätiedostoyhteyden Symbian-alustalle tarjoavan ohjelmiston vaatimukset ja tehtiin alustava suunnitelma ohjelmiston toteuttamiseksi. Läpinäkyvä tiedostojen etäkäyttö vaatii tiedostojenjakoprotokollan toteuttamista sovelluksille yhteisen tiedostosaantimekanismin alle. Tiedostojen etäkäyttö voi perustua eri tiedostojenjakoprotokolliin kuten IP:n päällä toimiviin NFS:ään tai CIFS:ään. Langattomuuden aiheuttamat rajoitukset laitteissa ja tiedonsiirrossa saattavat vähentää sovellutuksen käytettävyyttä ja on huomioitava ohjelmistoa toteutettaessa. Symbian-alusta perustuu asiakas-palvelin arkkitehtuuriin, jossa asiakassovellukset käyttävät tiedostopalveluita yhteisen tiedostopalvelimen kautta. Etätiedostoyhteys on mahdollista toteuttaa liittämällä uusi kirjastomoduuli tiedostopalvelimeen. Protokollan toteuttavan moduulin on muunnettava protokollan viestit tiedostopalvelimelle sopiviksi huolehtien samalla muista samanaikaisista tiedostotapahtumista. Suunniteltu moduulin arkkitehtuuri mahdollistaa eri protokollavaihtoehtojen käyttämisen etätiedostoyhteyden toteuttamiseen.

Maintenance process development for remote administration of network servers

Maintenance is a part of system development and it is possible to develop operation models for accomplishing maintenance tasks. These models can be applied to individual maintenance tasks, maintenance projects and version management. Beneficial operation models makes maintenance more effective and they assist in managing various changes. The purpose of this thesis was to develop a maintenance process which can be used to remote administer network servers. This consisted of defining those operation models and technical specifications which enable to set up, manage changes, maintain and monitor resources of information systems that are located in several different sites. At first in this thesis the needs of the process were determined and requirements were defined based on those needs. The meaning of processes in maintenance of information systems, maintenance workflows and challenges were studied. Then current practical problems and disadvantages of maintenance work were analyzed in order to focus the development to proper issues. Because available operation models did not cover all the recent needs, new maintenance process which fulfilled the requirements was developed.

Inversion and assimilation methods with applications in geophysical remote sensing

Stratospheric ozone can be measured accurately using a limb scatter remote sensing technique at the UV-visible spectral region of solar light. The advantages of this technique includes a good vertical resolution and a good daytime coverage of the measurements. In addition to ozone, UV-visible limb scatter measurements contain information about NO2, NO3, OClO, BrO and aerosols. There are currently several satellite instruments continuously scanning the atmosphere and measuring the UVvisible region of the spectrum, e.g., the Optical Spectrograph and Infrared Imager System (OSIRIS) launched on the Odin satellite in February 2001, and the Scanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY (SCIAMACHY) launched on Envisat in March 2002. Envisat also carries the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument, which also measures limb-scattered sunlight under bright limb occultation conditions. These conditions occur during daytime occultation measurements. The global coverage of the satellite measurements is far better than any other ozone measurement technique, but still the measurements are sparse in the spatial domain. Measurements are also repeated relatively rarely over a certain area, and the composition of the Earth’s atmosphere changes dynamically. Assimilation methods are therefore needed in order to combine the information of the measurements with the atmospheric model. In recent years, the focus of assimilation algorithm research has turned towards filtering methods. The traditional Extended Kalman filter (EKF) method takes into account not only the uncertainty of the measurements, but also the uncertainty of the evolution model of the system. However, the computational cost of full blown EKF increases rapidly as the number of the model parameters increases. Therefore the EKF method cannot be applied directly to the stratospheric ozone assimilation problem. The work in this thesis is devoted to the development of inversion methods for satellite instruments and the development of assimilation methods used with atmospheric models.

Development of remote monitoring process tools for biomass fired bubbling fluidized bed boilers

Remote monitoring of a power boiler allows the supplying company to make sure that equipment is used as supposed to and gives a good chance for process optimization. This improves co-operation between the supplier and the customer and creates an aura of trust that helps securing future contracts. Remote monitoring is already in use with recovery boilers but the goal is to expand especially to biomass-fired BFB-boilers. To make remote monitoring possible, data has to be measured reliably on site and the link between the power plant and supplying company’s server has to work reliably. Data can be gathered either with the supplier’s sensors or with measurements originally installed in the power plant if the plant in question is not originally built by the supplying company. Main goal in remote monitoring is process optimization and avoiding unnecessary accidents. This can be achieved for instance by following the efficiency curves and fouling in different parts of the process and comparing them to past values. The final amount of calculations depends on the amount of data gathered. Sudden changes in efficiency or fouling require further notice and in such a case it’s important that dialogue toward the power plant in question also works.

Underwater Remote Welding Technology for Offshore Structures

The construction of offshore structures, equipment and devices requires a high level of mechanical reliability in terms of strength, toughness and ductility. One major site for mechanical failure, the weld joint region, needs particularly careful examination, and weld joint quality has become a major focus of research in recent times. Underwater welding carried out offshore faces specific challenges affecting the mechanical reliability of constructions completed underwater. The focus of this thesis is on improvement of weld quality of underwater welding using control theory. This research work identifies ways of optimizing the welding process parameters of flux cored arc welding (FCAW) during underwater welding so as to achieve desired weld bead geometry when welding in a water environment. The weld bead geometry has no known linear relationship with the welding process parameters, which makes it difficult to determine a satisfactory weld quality. However, good weld bead geometry is achievable by controlling the welding process parameters. The doctoral dissertation comprises two sections. The first part introduces the topic of the research, discusses the mechanisms of underwater welding and examines the effect of the water environment on the weld quality of wet welding. The second part comprises four research papers examining different aspects of underwater wet welding and its control and optimization. Issues considered include the effects of welding process parameters on weld bead geometry, optimization of FCAW process parameters, and design of a control system for the purpose of achieving a desired bead geometry that can ensure a high level of mechanical reliability in welded joints of offshore structures. Artificial neural network systems and a fuzzy logic controller, which are incorporated in the control system design, and a hybrid of fuzzy and PID controllers are the major control dynamics used. This study contributes to knowledge of possible solutions for achieving similar high weld quality in underwater wet welding as found with welding in air. The study shows that carefully selected steels with very low carbon equivalent and proper control of the welding process parameters are essential in achieving good weld quality. The study provides a platform for further research in underwater welding. It promotes increased awareness of the need to improve the quality of underwater welding for offshore industries and thus minimize the risk of structural defects resulting from poor weld quality.

An Economic Analysis of Wind Energy Production in the Remote Areas of Quebec.

Rapport de recherche

Remote Sensing the Signatures of Upwelling in South Eastern Arabian Sea

In this thesis, a variety of available satellite data products have been made use of to bring out a synergistic analysis on the upwelling phenomenon in SEAS. Basic concepts of remote sensing, upwelling and linked oceanography topics have been dealt in this work .Auxiliary data products utilized in this study are described in chapter 2. The climatological monthly variability of the upwelling signatures are detailed under chapter 3. Chapter 4 presents the forcing factors that trigger the upwelling process in SEAS. Chapter 5 describes the oceanic response to the forcing factors with respect to the SST cooling and CHLA blooms. Chapter 6 presents the heat budget of the region and the variability of heat budget terms with respect to upwelling. Chapter 7 describes the inter-annual variability of upwelling intensity in SEAS and the influence of climatic events on upwelling.