Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

The main challenge of new information technologies is to retrieve intelligible information from the large volume of digital data gathered every day. Among the variety of existing data sources, the satellites continuously observing the surface of the Earth are key to the monitoring of our environment. The new generation of satellite sensors are tremendously increasing the possibilities of applications but also increasing the need for efficient processing methodologies in order to extract information relevant to the users' needs in an automatic or semi-automatic way. This is where machine learning comes into play to transform complex data into simplified products such as maps of land-cover changes or classes by learning from data examples annotated by experts. These annotations, also called labels, may actually be difficult or costly to obtain since they are established on the basis of ground surveys. As an example, it is extremely difficult to access a region recently flooded or affected by wildfires. In these situations, the detection of changes has to be done with only annotations from unaffected regions. In a similar way, it is difficult to have information on all the land-cover classes present in an image while being interested in the detection of a single one of interest. These challenging situations are called novelty detection or one-class classification in machine learning. In these situations, the learning phase has to rely only on a very limited set of annotations, but can exploit the large set of unlabeled pixels available in the images. This setting, called semi-supervised learning, allows significantly improving the detection. In this Thesis we address the development of methods for novelty detection and one-class classification with few or no labeled information. The proposed methodologies build upon the kernel methods, which take place within a principled but flexible framework for learning with data showing potentially non-linear feature relations. The thesis is divided into two parts, each one having a different assumption on the data structure and both addressing unsupervised (automatic) and semi-supervised (semi-automatic) learning settings. The first part assumes the data to be formed by arbitrary-shaped and overlapping clusters and studies the use of kernel machines, such as Support Vector Machines or Gaussian Processes. An emphasis is put on the robustness to noise and outliers and on the automatic retrieval of parameters. Experiments on multi-temporal multispectral images for change detection are carried out using only information from unchanged regions or none at all. The second part assumes high-dimensional data to lie on multiple low dimensional structures, called manifolds. We propose a method seeking a sparse and low-rank representation of the data mapped in a non-linear feature space. This representation allows us to build a graph, which is cut into several groups using spectral clustering. For the semi-supervised case where few labels of one class of interest are available, we study several approaches incorporating the graph information. The class labels can either be propagated on the graph, constrain spectral clustering or used to train a one-class classifier regularized by the given graph. Experiments on the unsupervised and oneclass classification of hyperspectral images demonstrate the effectiveness of the proposed approaches

Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines

Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise unsupervised (UND). SSND can be seen as an unbalanced classification between labeled and unlabeled samples using the Cost-Sensitive Support Vector Machine (CS-SVM). UND assumes novelties in low density regions and can be approached using the One-Class SVM (OC-SVM). We propose here to use nested entire solution path algorithms for the OC-SVM and CS-SVM in order to accelerate the parameter selection and alleviate the dependency to labeled ``changed'' samples. Experiments are performed on two multitemporal change detection datasets (flood and fire detection) and the performance of the two methods proposed compared

Unsupervised Change Detection via Hierarchical Support Vector Clustering

When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of nested hierarchical support vector clustering descriptions, select the appropriate one using a cluster validity measure and finally merge the clusters into two classes, corresponding to changed and unchanged areas. Experiments on two multispectral datasets confirm the power and appropriateness of the proposed system

Semi-Supervised Novelty Detection using SVM entire solution path

Very often, the only reliable information available to perform change detection is the description of some unchanged regions. Since sometimes these regions do not contain all the relevant information to identify their counterpart (the changes), we consider the use of unlabeled data to perform Semi-Supervised Novelty detection (SSND). SSND can be seen as an unbalanced classification problem solved using the Cost-Sensitive Support Vector Machine (CS-SVM), but this requires a heavy parameter search. We propose here to use entire solution path algorithms for the CS-SVM in order to facilitate and accelerate the parameter selection for SSND. Two algorithms are considered and evaluated. The first one is an extension of the CS-SVM algorithm that returns the entire solution path in a single optimization. This way, the optimization of a separate model for each hyperparameter set is avoided. The second forces the solution to be coherent through the solution path, thus producing classification boundaries that are nested (included in each other). We also present a low density criterion for selecting the optimal classification boundaries, thus avoiding the recourse to cross-validation that usually requires information about the ``change'' class. Experiments are performed on two multitemporal change detection datasets (flood and fire detection). Both algorithms tracing the solution path provide similar performances than the standard CS-SVM while being significantly faster. The low density criterion proposed achieves results that are close to the ones obtained by cross-validation, but without using information about the changes

Robust Phase-Correlation based Registration of Airborne Videos using Motion Estimation

This paper presents a robust algorithm for the registration of airborne video sequences with reference images from a different source (airborne or satellite), based on phase-correlation. Phase-correlations using Fourier-Melin Invariant (FMI) descriptors allow to retrieve the rigid transformation parameters in a fast and non-iterative way. The robustness to multi-sources images is improved by an enhanced image representation based on the gradient norm and the extrapolation of registration parameters between frames by motion estimation. A phase-correlation score, indicator of the registration quality, is introduced to regulate between motion estimation only and frame-toreference image registration. Our Robust Phase-Correlation registration algorithm using Motion Estimation (RPCME) is compared with state-of-the-art Mutual Information (MI) algorithm on two different airborne videos. RPCME algorithm registered most of the frames accurately, retrieving much better orientation than MI. Our algorithm shows robustness and good accuracy to multisource images with the advantage of being a direct (non-iterative) method

Sur la question du pôle de froid

de Morsier Frank. Sur la question du pôle de froid. In: Le Globe. Revue genevoise de géographie, tome 12, 1873. pp. 143-144

A propos du voyage du lieutenant américain Schwatka à la terre du Roi-Guillaume

de Morsier Frank. A propos du voyage du lieutenant américain Schwatka à la terre du Roi-Guillaume. In: Le Globe. Revue genevoise de géographie, tome 20, 1881. pp. 135-136

Registration of multi-modal and multi-temporal remote sensing images and introduction to novelty detection

The database of satellite images covering the earth is growing extremely quickly and represents an important amount of data. The extension of this huge amount of data with images from very different sources and time of acquisition can provide a wide range of applications. The climate change monitoring could be more easily achieved or Natural disasters (deforestation, floods, dry rivers, earthquake) characterized by Unmanned Aerial Vehicle (UAV), allowing a rapid intervention. This project aims to realize a robust registration of images from different modalities (view angle, resolution, sensors sensitivity) having temporal changes and possible "novelties". A novelty would be a change not due to normal season changes, like with new buildings or natural disaster. An introduction to the application of novelty detection on multi-modal and multi-temporal images is discussed based on recent research on novelty detection. Video recordings from UAV are provided by RUAG. Different flight conditions have been realized to get a wide range of images, in terms of content, view angle, field of view or stabilization. The images, taken as reference, are from satellite (SPOT 5) and airborne (Google Map) sources. A registration algorithm based on the phase-correlation allowing to retrieve rotation, scaling and translation between two images is proposed. It handles the multi-modal characteristics of the images by correcting the perspective deformation, exploiting the redundancy of video sequence and adaptively choose between the image edges or the extraction of large structure from the image depending on the image frequency content. The results are encouraging for the possibility of novelty detection. The algorithm is sensitive to the initial conditions but accurate registration with stabilized and unstabilized flights is achieved using Google Maps. Less precise registration is achieved with SPOT images coming from their lower resolution

Le mont Saint-Elias dans l'Alaska

de Morsier Frank. Le mont Saint-Elias dans l'Alaska. In: Le Globe. Revue genevoise de géographie, tome 27, 1888. pp. 14-17

Les expéditions arctiques en 1881. Extrait des Petermanrn's Mittheilungen. Vol. XXVIII, N° 1, janvier 1882.

de Morsier Frank. Les expéditions arctiques en 1881. Extrait des Petermanrn's Mittheilungen. Vol. XXVIII, N° 1, janvier 1882. In: Le Globe. Revue genevoise de géographie, tome 21, 1882. pp. 60-79