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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.

This paper presents an algorithm for near-real time registration of airborne video sequences with reference images from a different sensor type. Phase-correlation 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 obtained by an enhanced image representation based on the gradient norm and by the extrapolation of registration parameters by motion estimation between frames. A phase-correlation score, indicator of the registration quality, is introduced to regulate between frame-toreference image registration and extrapolation from previous frames only. Our Robust Phase-Correlation based registration algorithm using Motion Estimation (RPCME) is compared with a Mutual Information (MI) algorithm for the registration of two different panchromatic airborne videos with Geoeye reference images. The RPCME algorithm registered most of the frames accurately, retrieving much better orientation than MI. It shows robustness and good accuracy to multisource images with the advantage of being a direct (non-iterative) method.

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