Remote sensing; Change detection; Multi-source; Kernel methods; Very high resolution; Ground cover classification; Environmental monitoring; Hyperspectral, Synthetic aperture radar, LiDAR
Trolliet Maxime, Tuia Devis, Volpi Michele (2013), Classification of urban multi-angular image sequences by aligning their manifolds, in Joint Urban Remote Sensing Event JURSE
, Sao Paolo, BRA.
Volpi Michele, Petropoulos Gerge P., Kanevski Mikhail (2013), Flooding Extent Cartography with Landsat TM Imagery and Regularized Kernel Fisher' s Discriminant Analysis, in Computers and Geosciences
, 57, 24-31.
Volpi Michele, Matasci Giona, Kanevski Mikhail, Tuia Devis (2013), Multi-view feature extraction for hyperspectral image classification, in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
, Bruges, B.
Tuia Devis, Volpi Michele, Dalla Mura Mauro, Rakotomamonjy Alain, Flamary Rémi, Create the relevant spatial filterbank in the hyperspectral jungle,, in IEEE International Geosciences and Remote Sensing Symposium IGARSS,
, Melbourne, AUS.
Tuia Devis, Trolliet Maxime, Volpi Michele, Multisensor alignment of image manifolds, in IEEE International Geosciences and Remote Sensing Symposium IGARSS
, Melbourne, AUS.
Volpi Michele, de Morsier Frank, Camps-Valls Gustavo, Kanevski Mikhail, Tuia Devis, Multi-sensor change detection based on nonlinear canonical correlations, in EEE International Geosciences and Remote Sensing Symposium IGARSS
, Melbourne, AUS.
One of the most challenging problems in remote sensing and Earth observation is the detection of changes occurred between two acquisitions of the same geographical area at different times. Such detections are crucial for environmental and urban monitoring, post-catastrophe assessment and natural hazard mapping. Many techniques that aim at addressing the problem starting from different assumptions exist, and among which difference image analysis and post classification comparisons are the most utilized.In the current “KernelCD” project (SNSF Grant No. 200021-126505), the problem of detecting changes in optical images was successfully addressed by applying methods coming from the machine learning community. In particular, supervised and unsupervised kernel-based techniques were developed specifically for change detection. For the supervised methods, the approaches proposed were designed with accent on accuracy, while light computational load was preferred for the unsupervised ones. Finally, robustness was the main criterion for the success of feature extraction techniques, last topic proposed in the current “KernelCD” project. Despite the success of this first part of the project, the potential of such developments for the applicative domain remains underexploited. One of the main limiting factors remains the recourse to single-source sensors. In order to fully accomplish the aim of “KernelCD”, new methods specific for multi-source data need to be developed. To this end, we ask for a second part of “KernelCD” (“KernelCD” phase 2) in this proposal.In this second part of the project, a one year prolongation, we aim at developing approaches specific to multi-source, multi-modal and multi-temporal remote sensing image processing, with focus to accurate and robust change detection. On the one hand, the methods designed will be able to deal with noisy environments and will be robust with respect to heterogeneous input sources. But on the other hand, they will maintain strict accuracy standards and information extraction capabilities. As for the current “KernelCD” project, these problems will be tackled by considering the most recent developments in the machine learning and pattern recognition communities. Kernel-methods have been poorly documented in the remote sensing community for multi-temporal data-fusion applications: we aim at filling this gap by building on the knowledge acquired from the current “KernelCD” project, and by proposing high-level research by introducing novel techniques for change detection under these conditions. Particular attention will be paid to kernel-based nonlinear feature extraction / manifold learning and dimensionality reduction methods, which will be applied to data-fusion tasks (thus adopting a feature-level fusion approach). On these bases, once the data structure is described, a comprehensive change detector (generating change maps) will be developed. Considering of heterogeneous sources available is a compulsory step, as the changes react differently depending on the type of sensor. For the new detector, the lacks of information of a specific sensor will no more constitute a limitation. This continuation allows a natural ending of the “KernelCD” project: the development of novel advanced techniques dedicated to multi-temporal multi-source data processing. The “KernelCD” phase 2 starts from the achievements of its precursor, which have been appreciated at international level by publications, invited talks and fruitful collaborations. It is therefore a natural evolution of the project that will increase its positive impact on to both theoretical and applicative scientific communities.