Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

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.

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.

Evaluating effects of spectral training data distribution on continuous field mapping performance

Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance. We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models. Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

Structural characterization of rockfall sources in Yosemite Valley from remote sensing data and field surveys

Yosemite Valley poses significant rockfall hazard and related risk due to its glacially steepened walls and approximately 4 million visitors annually. To assess rockfall hazard, it is necessary to evaluate the geologic structure that contributes to the destabilization of rockfall sources and locate the most probable future source areas. Coupling new remote sensing techniques (Terrestrial Laser Scanning, Aerial Laser Scanning) and traditional field surveys, we investigated the regional geologic and structural setting, the orientation of the primary discontinuity sets for large areas of Yosemite Valley, and the specific discontinuity sets present at active rockfall sources. This information, combined with better understanding of the geologic processes that contribute to the progressive destabilization and triggering of granitic rock slabs, contributes to a more accurate rockfall susceptibility assessment for Yosemite Valley and elsewhere.

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.

Rockfall susceptibility assessment and remote geological mapping with LiDAR point clouds

Characterizing the geological features and structures in three dimensions over inaccessible rock cliffs is needed to assess natural hazards such as rockfalls and rockslides and also to perform investigations aimed at mapping geological contacts and building stratigraphy and fold models. Indeed, the detailed 3D data, such as LiDAR point clouds, allow to study accurately the hazard processes and the structure of geologic features, in particular in vertical and overhanging rock slopes. Thus, 3D geological models have a great potential of being applied to a wide range of geological investigations both in research and applied geology projects, such as mines, tunnels and reservoirs. Recent development of ground-based remote sensing techniques (LiDAR, photogrammetry and multispectral / hyperspectral images) are revolutionizing the acquisition of morphological and geological information. As a consequence, there is a great potential for improving the modeling of geological bodies as well as failure mechanisms and stability conditions by integrating detailed remote data. During the past ten years several large rockfall events occurred along important transportation corridors where millions of people travel every year (Switzerland: Gotthard motorway and railway; Canada: Sea to sky highway between Vancouver and Whistler). These events show that there is still a lack of knowledge concerning the detection of potential rockfalls, making mountain residential settlements and roads highly risky. It is necessary to understand the main factors that destabilize rocky outcrops even if inventories are lacking and if no clear morphological evidences of rockfall activity are observed. In order to increase the possibilities of forecasting potential future landslides, it is crucial to understand the evolution of rock slope stability. Defining the areas theoretically most prone to rockfalls can be particularly useful to simulate trajectory profiles and to generate hazard maps, which are the basis for land use planning in mountainous regions. The most important questions to address in order to assess rockfall hazard are: Where are the most probable sources for future rockfalls located? What are the frequencies of occurrence of these rockfalls? I characterized the fracturing patterns in the field and with LiDAR point clouds. Afterwards, I developed a model to compute the failure mechanisms on terrestrial point clouds in order to assess the susceptibility to rockfalls at the cliff scale. Similar procedures were already available to evaluate the susceptibility to rockfalls based on aerial digital elevation models. This new model gives the possibility to detect the most susceptible rockfall sources with unprecented detail in the vertical and overhanging areas. The results of the computation of the most probable rockfall source areas in granitic cliffs of Yosemite Valley and Mont-Blanc massif were then compared to the inventoried rockfall events to validate the calculation methods. Yosemite Valley was chosen as a test area because it has a particularly strong rockfall activity (about one rockfall every week) which leads to a high rockfall hazard. The west face of the Dru was also chosen for the relevant rockfall activity and especially because it was affected by some of the largest rockfalls that occurred in the Alps during the last 10 years. Moreover, both areas were suitable because of their huge vertical and overhanging cliffs that are difficult to study with classical methods. Limit equilibrium models have been applied to several case studies to evaluate the effects of different parameters on the stability of rockslope areas. The impact of the degradation of rockbridges on the stability of large compartments in the west face of the Dru was assessed using finite element modeling. In particular I conducted a back-analysis of the large rockfall event of 2005 (265'000 m3) by integrating field observations of joint conditions, characteristics of fracturing pattern and results of geomechanical tests on the intact rock. These analyses improved our understanding of the factors that influence the stability of rock compartments and were used to define the most probable future rockfall volumes at the Dru. Terrestrial laser scanning point clouds were also successfully employed to perform geological mapping in 3D, using the intensity of the backscattered signal. Another technique to obtain vertical geological maps is combining triangulated TLS mesh with 2D geological maps. At El Capitan (Yosemite Valley) we built a georeferenced vertical map of the main plutonio rocks that was used to investigate the reasons for preferential rockwall retreat rate. Additional efforts to characterize the erosion rate were made at Monte Generoso (Ticino, southern Switzerland) where I attempted to improve the estimation of long term erosion by taking into account also the volumes of the unstable rock compartments. Eventually, the following points summarize the main out puts of my research: The new model to compute the failure mechanisms and the rockfall susceptibility with 3D point clouds allows to define accurately the most probable rockfall source areas at the cliff scale. The analysis of the rockbridges at the Dru shows the potential of integrating detailed measurements of the fractures in geomechanical models of rockmass stability. The correction of the LiDAR intensity signal gives the possibility to classify a point cloud according to the rock type and then use this information to model complex geologic structures. The integration of these results, on rockmass fracturing and composition, with existing methods can improve rockfall hazard assessments and enhance the interpretation of the evolution of steep rockslopes. -- La caractérisation de la géologie en 3D pour des parois rocheuses inaccessibles est une étape nécessaire pour évaluer les dangers naturels tels que chutes de blocs et glissements rocheux, mais aussi pour réaliser des modèles stratigraphiques ou de structures plissées. Les modèles géologiques 3D ont un grand potentiel pour être appliqués dans une vaste gamme de travaux géologiques dans le domaine de la recherche, mais aussi dans des projets appliqués comme les mines, les tunnels ou les réservoirs. Les développements récents des outils de télédétection terrestre (LiDAR, photogrammétrie et imagerie multispectrale / hyperspectrale) sont en train de révolutionner l'acquisition d'informations géomorphologiques et géologiques. Par conséquence, il y a un grand potentiel d'amélioration pour la modélisation d'objets géologiques, ainsi que des mécanismes de rupture et des conditions de stabilité, en intégrant des données détaillées acquises à distance. Pour augmenter les possibilités de prévoir les éboulements futurs, il est fondamental de comprendre l'évolution actuelle de la stabilité des parois rocheuses. Définir les zones qui sont théoriquement plus propices aux chutes de blocs peut être très utile pour simuler les trajectoires de propagation des blocs et pour réaliser des cartes de danger, qui constituent la base de l'aménagement du territoire dans les régions de montagne. Les questions plus importantes à résoudre pour estimer le danger de chutes de blocs sont : Où se situent les sources plus probables pour les chutes de blocs et éboulement futurs ? Avec quelle fréquence vont se produire ces événements ? Donc, j'ai caractérisé les réseaux de fractures sur le terrain et avec des nuages de points LiDAR. Ensuite, j'ai développé un modèle pour calculer les mécanismes de rupture directement sur les nuages de points pour pouvoir évaluer la susceptibilité au déclenchement de chutes de blocs à l'échelle de la paroi. Les zones sources de chutes de blocs les plus probables dans les parois granitiques de la vallée de Yosemite et du massif du Mont-Blanc ont été calculées et ensuite comparés aux inventaires des événements pour vérifier les méthodes. Des modèles d'équilibre limite ont été appliqués à plusieurs cas d'études pour évaluer les effets de différents paramètres sur la stabilité des parois. L'impact de la dégradation des ponts rocheux sur la stabilité de grands compartiments de roche dans la paroi ouest du Petit Dru a été évalué en utilisant la modélisation par éléments finis. En particulier j'ai analysé le grand éboulement de 2005 (265'000 m3), qui a emporté l'entier du pilier sud-ouest. Dans le modèle j'ai intégré des observations des conditions des joints, les caractéristiques du réseau de fractures et les résultats de tests géoméchaniques sur la roche intacte. Ces analyses ont amélioré l'estimation des paramètres qui influencent la stabilité des compartiments rocheux et ont servi pour définir des volumes probables pour des éboulements futurs. Les nuages de points obtenus avec le scanner laser terrestre ont été utilisés avec succès aussi pour produire des cartes géologiques en 3D, en utilisant l'intensité du signal réfléchi. Une autre technique pour obtenir des cartes géologiques des zones verticales consiste à combiner un maillage LiDAR avec une carte géologique en 2D. A El Capitan (Yosemite Valley) nous avons pu géoréferencer une carte verticale des principales roches plutoniques que j'ai utilisé ensuite pour étudier les raisons d'une érosion préférentielle de certaines zones de la paroi. D'autres efforts pour quantifier le taux d'érosion ont été effectués au Monte Generoso (Ticino, Suisse) où j'ai essayé d'améliorer l'estimation de l'érosion au long terme en prenant en compte les volumes des compartiments rocheux instables. L'intégration de ces résultats, sur la fracturation et la composition de l'amas rocheux, avec les méthodes existantes permet d'améliorer la prise en compte de l'aléa chute de pierres et éboulements et augmente les possibilités d'interprétation de l'évolution des parois rocheuses.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.