Publications by Maurice Borgeaud

Small satellites for space science A COSPAR scientific roadmap

This is a COSPAR roadmap to advance the frontiers of science through innovation and international collaboration using small satellites. The world of small satellites is evolving quickly and an opportunity exists to leverage these developments to make scien ...

ELSEVIER SCI LTD2019

Non-linear Low-rank and Sparse Representation for Hyperspectral Image Analysis

Jean-Philippe Thiran, Devis Tuia, Frank Grégoire Jean de Morsier, Maurice Borgeaud, Volker Gass

In this paper, we tackle the problem of unsupervised classification of hyperspectral images. We propose a clustering method based on graphs representing the data structure, which is assumed to be an union of multiple manifolds. The method constraints the p ...

2014

Crop Backscatter Modeling And Soil Moisture Estimation With Support Vector Regression

Jean-Philippe Thiran, Devis Tuia, Maurice Borgeaud, Jelena Stamenkovic

In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during th ...

Ieee2014

Detection of Floods In Sar Images with Non-Linear Kernel Clustering and Topographic Prior

Jean-Philippe Thiran, Devis Tuia, Frank Grégoire Jean de Morsier, Maurice Borgeaud

After a major flood catastrophe, a precious information is the delineation of the affected areas. Remote sensing imagery, especially synthetic aperture radar, allows to obtain a global and complete view of the situation. However, the detection of the flood ...

2013

Estimation of Soil Moisture from Airborne Hyperspectral Imagery with Support Vector Regression

Jean-Philippe Thiran, Devis Tuia, Frank Grégoire Jean de Morsier, Maurice Borgeaud, Jelena Stamenkovic

In this paper, we propose to estimate soil moisture in bare soils directly from hyperspectral imagery using support vector regression (nu-SVR). nu-SVR is a supervised non-parametric learning technique, e.g. making no assumption on the underlying data distr ...

2013

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

Jean-Philippe Thiran, Frank Grégoire Jean de Morsier, Maurice Borgeaud, Volker Gass

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 al ...

Springer-Verlag2013

Multisource Clustering of Remote Sensing Images With Entropy-Based Dempster-Shafer Fusion

Jean-Philippe Thiran, Devis Tuia, Frank Grégoire Jean de Morsier, Maurice Borgeaud, Tsirihasina Sitraka Ranoeliarivao

In this paper, we propose a strategy for fusing clustering maps obtained with different remote sensing sources. Dempster- Shafer (DS) Theory is a powerful fusion method that allows to combine classifications from different sources and handles ignorance, im ...

IEEE2013

Semi-Supervised Novelty Detection using SVM entire solution path

Jean-Philippe Thiran, Devis Tuia, Frank Grégoire Jean de Morsier, Maurice Borgeaud, Volker Gass

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 ...

Institute of Electrical and Electronics Engineers2013

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

Jean-Philippe Thiran, Frank Grégoire Jean de Morsier, Maurice Borgeaud, Volker Gass

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 ...

Ieee2012

Unsupervised Change Detection via Hierarchical Support Vector Clustering

Jean-Philippe Thiran, Devis Tuia, Frank Grégoire Jean de Morsier, Maurice Borgeaud, Volker Gass

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 n ...

2012