Online Graph-Based Change Point Detection in Multiband Image Sequences
arXiv:2006.14033 · doi:10.23919/Eusipco47968.2020.9287747
Abstract
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.
References in corpus (9)
- The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
- Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
- Autoregressive Moving Average Graph Filtering
- Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
- A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing
- Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability
- A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability
- Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
- On-the-fly Approximation of Multivariate Total Variation Minimization