Semi-supervised remote sensing image classification with cluster kernels

A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

Characterization of gravitational rock slope deformations at different spatial scales based on field, remote sensing and numerical approaches

Rock slope instabilities such as rock slides, rock avalanche or deep-seated gravitational slope deformations are widespread in Alpine valleys. These phenomena represent at the same time a main factor that control the mountain belts erosion and also a significant natural hazard that creates important losses to the mountain communities. However, the potential geometrical and dynamic connections linking outcrop and slope-scale instabilities are often unknown. A more detailed definition of the potential links will be essential to improve the comprehension of the destabilization processes and to dispose of a more complete hazard characterization of the rock instabilities at different spatial scales. In order to propose an integrated approach in the study of the rock slope instabilities, three main themes were analysed in this PhD thesis: (1) the inventory and the spatial distribution of rock slope deformations at regional scale and their influence on the landscape evolution, (2) the influence of brittle and ductile tectonic structures on rock slope instabilities development and (3) the characterization of hazard posed by potential rock slope instabilities through the development of conceptual instability models. To prose and integrated approach for the analyses of these topics, several techniques were adopted. In particular, high resolution digital elevation models revealed to be fundamental tools that were employed during the different stages of the rock slope instability assessment. A special attention was spent in the application of digital elevation model for detailed geometrical modelling of past and potential instabilities and for the rock slope monitoring at different spatial scales. Detailed field analyses and numerical models were performed to complete and verify the remote sensing approach. In the first part of this thesis, large slope instabilities in Rhone valley (Switzerland) were mapped in order to dispose of a first overview of tectonic and climatic factors influencing their distribution and their characteristics. Our analyses demonstrate the key influence of neotectonic activity and the glacial conditioning on the spatial distribution of the rock slope deformations. Besides, the volumes of rock instabilities identified along the main Rhone valley, were then used to propose the first estimate of the postglacial denudation and filling of the Rhone valley associated to large gravitational movements. In the second part of the thesis, detailed structural analyses of the Frank slide and the Sierre rock avalanche were performed to characterize the influence of brittle and ductile tectonic structures on the geometry and on the failure mechanism of large instabilities. Our observations indicated that the geometric characteristics and the variation of the rock mass quality associated to ductile tectonic structures, that are often ignored landslide study, represent important factors that can drastically influence the extension and the failure mechanism of rock slope instabilities. In the last part of the thesis, the failure mechanisms and the hazard associated to five potential instabilities were analysed in detail. These case studies clearly highlighted the importance to incorporate different analyses and monitoring techniques to dispose of reliable and hazard scenarios. This information associated to the development of a conceptual instability model represents the primary data for an integrated risk management of rock slope instabilities. - Les mouvements de versant tels que les chutes de blocs, les éboulements ou encore les phénomènes plus lents comme les déformations gravitaires profondes de versant représentent des manifestations courantes en régions montagneuses. Les mouvements de versant sont à la fois un des facteurs principaux contrôlant la destruction progressive des chaines orogéniques mais aussi un danger naturel concret qui peut provoquer des dommages importants. Pourtant, les phénomènes gravitaires sont rarement analysés dans leur globalité et les rapports géométriques et mécaniques qui lient les instabilités à l'échelle du versant aux instabilités locales restent encore mal définis. Une meilleure caractérisation de ces liens pourrait pourtant représenter un apport substantiel dans la compréhension des processus de déstabilisation des versants et améliorer la caractérisation des dangers gravitaires à toutes les échelles spatiales. Dans le but de proposer un approche plus globale à la problématique des mouvements gravitaires, ce travail de thèse propose trois axes de recherche principaux: (1) l'inventaire et l'analyse de la distribution spatiale des grandes instabilités rocheuses à l'échelle régionale, (2) l'analyse des structures tectoniques cassantes et ductiles en relation avec les mécanismes de rupture des grandes instabilités rocheuses et (3) la caractérisation des aléas rocheux par une approche multidisciplinaire visant à développer un modèle conceptuel de l'instabilité et une meilleure appréciation du danger . Pour analyser les différentes problématiques traitées dans cette thèse, différentes techniques ont été utilisées. En particulier, le modèle numérique de terrain s'est révélé être un outil indispensable pour la majorité des analyses effectuées, en partant de l'identification de l'instabilité jusqu'au suivi des mouvements. Les analyses de terrain et des modélisations numériques ont ensuite permis de compléter les informations issues du modèle numérique de terrain. Dans la première partie de cette thèse, les mouvements gravitaires rocheux dans la vallée du Rhône (Suisse) ont été cartographiés pour étudier leur répartition en fonction des variables géologiques et morphologiques régionales. En particulier, les analyses ont mis en évidence l'influence de l'activité néotectonique et des phases glaciaires sur la distribution des zones à forte densité d'instabilités rocheuses. Les volumes des instabilités rocheuses identifiées le long de la vallée principale ont été ensuite utilisés pour estimer le taux de dénudations postglaciaire et le remplissage de la vallée du Rhône lié aux grands mouvements gravitaires. Dans la deuxième partie, l'étude de l'agencement structural des avalanches rocheuses de Sierre (Suisse) et de Frank (Canada) a permis de mieux caractériser l'influence passive des structures tectoniques sur la géométrie des instabilités. En particulier, les structures issues d'une tectonique ductile, souvent ignorées dans l'étude des instabilités gravitaires, ont été identifiées comme des structures très importantes qui contrôlent les mécanismes de rupture des instabilités à différentes échelles. Dans la dernière partie de la thèse, cinq instabilités rocheuses différentes ont été étudiées par une approche multidisciplinaire visant à mieux caractériser l'aléa et à développer un modèle conceptuel trois dimensionnel de ces instabilités. A l'aide de ces analyses on a pu mettre en évidence la nécessité d'incorporer différentes techniques d'analyses et de surveillance pour une gestion plus objective du risque associée aux grandes instabilités rocheuses.

Monitoring and predicting crop growth and analysing agricultural ecosystems by remote sensing

Selostus: Maatalousekosysteemien analysointi ja sadon ennustaminen kaukokartoituksen avulla

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.

Recent advances in remote sensing image processing

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.

The Use of Remote Sensing Techniques Compared to Traditional Archaeological Phase II Testing Methods: A Cost-Benefit Analysis, HR-524, 1986

Remote sensing was utilized in the Phase II Cultural Resources Investigation for this project in lieu of extensive excavations. The purpose of the present report is to compare the costs and benefits of the use of remote sensing to the hypothetical use of traditional excavation methods for this project. Estimates for this hypothetical situation are based on the project archaeologist's considerable past experience in conducting similar investigations. Only that part of the Phase II investigation involving field investigations is addressed in this report. Costs for literature review, laboratory analysis, report preparation, etc., are not included. The project manager proposed the use of this technique for the fol lowing logistic, safety and budgetary reasons.

Remote Sensing (LIDAR) for Management of Highway Assets for Safety, 2003

The main objective of this study was to utilize light detection and ranging (LIDAR) technology to obtain highway safety-related information. The safety needs of older drivers in terms of prolonged reaction times were taken into consideration. The tasks undertaken in this study were (1) identification of crashes that older drivers are more likely to be involved in, (2) identification of highway geometric features that are important in such crashes, (3) utilization of LIDAR data for obtaining information on the identified highway geometric features, and (4) assessment of the feasibility of using LIDAR data for such applications. A review of previous research indicated that older drivers have difficulty negotiating intersections, and it was recognized that intersection sight triangles were critical to safe intersection negotiation. LIDAR data were utilized to obtain information on potential sight distance obstructions at six selected intersections located on the Iowa Highway 1 corridor by conducting in-office line-of-sight analysis. Crash frequency, older driver involvement, and data availability were considerations in the selection of the six intersections. Results of the in-office analysis were then validated by visiting the intersections in the field. Sixty-six potential sight distance obstructions were identified by the line-of-sight analysis, out of which 62 (89.8%) were confirmed while four (5.8%) were not confirmed by the video. At least three (4.4%) potential sight distance obstructions were discovered in the video that were not detected by the line-of-sight analysis. The intersection with the highest crash frequency involving older drivers was correctly found to have obstructions located within the intersection sight triangles. Based on research results, it is concluded that LIDAR data can be utilized for identifying potential sight distance obstructions at intersections. The safety of older drivers can be enhanced by locating and rectifying intersections with obstructions in sight triangles.

Remote Sensing Change Analysis Methodology to Support Traffic Monitoring Programs, 2003

The Federal Highway Administration mandates that states collect traffic count information at specified intervals to meet the needs of the Highway Performance Monitoring System (HPMS). A manual land use change detection method was employed to determine the effects of land use change on traffic for Black Hawk County, Iowa, from 1994 to 2002. Results from land use change detection could enable redirecting traffic count activities and related data management resources to areas that are experiencing the greatest changes in land use and related traffic volume. Including a manual land use change detection process in the Iowa Department of Transportation’s traffic count program has the potential to improve efficiency by focusing monitoring activities in areas more likely to experience significant increase in traffic.

Remote Sensing Change Analysis Methodology to Support Traffic Monitoring Programs: Phase 2, 2004

The Center for Transportation Research and Education (CTRE) issued a report in July 2003, based on a sample study of the application of remote sensed image land use change detection to the methodology of traffic monitoring in Blackhawk County, Iowa. In summary, the results indicated a strong correlation and a statistically significant regression coefficient between the identification of built-up land use change areas from remote sensed data and corresponding changes in traffic patterns, expressed as vehicle miles traveled (VMT). Based on these results, the Iowa Department of Transportation (Iowa DOT) requested that CTRE expand the study area to five counties in the southwest quadrant of the state. These counties are scheduled for traffic counts in 2004, and the Iowa DOT desired the data to 1) evaluate the current methodology used to place the devices; 2) potentially influence the placement of traffic counting devices in areas of high built-up land use change; and 3) determine if opportunities exist to reduce the frequency and/or density of monitoring activity in lower trafficked rural areas of the state. This project is focused on the practical application of built-up land use change data for placement of traffic count data recording devices in five southwest Iowa counties.

Use of Remote Sensing for Collection of Data Elements for Linear Referencing Systems, 2001

This report evaluates the use of remotely sensed images in implementing the Iowa DOT LRS that is currently in the stages of system architecture. The Iowa Department of Transportation is investing a significant amount of time and resources into creation of a linear referencing system (LRS). A significant portion of the effort in implementing the system will be creation of a datum, which includes geographically locating anchor points and then measuring anchor section distances between those anchor points. Currently, system architecture and evaluation of different data collection methods to establish the LRS datum is being performed for the DOT by an outside consulting team.

Applying geostatistics for investigations of forest ecosystems using remote sensing imagery

Abstract