2 research outputs found
On-the-fly Approximation of Multivariate Total Variation Minimization
In the context of change-point detection, addressed by Total Variation
minimization strategies, an efficient on-the-fly algorithm has been designed
leading to exact solutions for univariate data. In this contribution, an
extension of such an on-the-fly strategy to multivariate data is investigated.
The proposed algorithm relies on the local validation of the Karush-Kuhn-Tucker
conditions on the dual problem. Showing that the non-local nature of the
multivariate setting precludes to obtain an exact on-the-fly solution, we
devise an on-the-fly algorithm delivering an approximate solution, whose
quality is controlled by a practitioner-tunable parameter, acting as a
trade-off between quality and computational cost. Performance assessment shows
that high quality solutions are obtained on-the-fly while benefiting of
computational costs several orders of magnitude lower than standard iterative
procedures. The proposed algorithm thus provides practitioners with an
efficient multivariate change-point detection on-the-fly procedure
Online Graph-Based Change Point Detection in Multiband Image Sequences
The automatic detection of changes or anomalies between multispectral and
hyperspectral images collected at different time instants is an active and
challenging research topic. To effectively perform change-point detection in
multitemporal images, it is important to devise techniques that are
computationally efficient for processing large datasets, and that do not
require knowledge about the nature of the changes. In this paper, we introduce
a novel online framework for detecting changes in multitemporal remote sensing
images. Acting on neighboring spectra as adjacent vertices in a graph, this
algorithm focuses on anomalies concurrently activating groups of vertices
corresponding to compact, well-connected and spectrally homogeneous image
regions. It fully benefits from recent advances in graph signal processing to
exploit the characteristics of the data that lie on irregular supports.
Moreover, the graph is estimated directly from the images using superpixel
decomposition algorithms. The learning algorithm is scalable in the sense that
it is efficient and spatially distributed. Experiments illustrate the detection
and localization performance of the method