Blue Noise Sampling and Nystrom Extension for Graph Based Change Detection
- Resource Type
- Conference
- Authors
- Jimenez-Sierra, David Alejandro; Benitez-Restrepo, Hernan Dario; Arce, Gonzalo R.; Florez-Ospina, Juan F.
- Source
- 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Geoscience and Remote Sensing Symposium IGARSS , 2021 IEEE International. :2895-2898 Jul, 2021
- Subject
- Aerospace
Geoscience
Photonics and Electrooptics
Signal Processing and Analysis
Measurement
Statistical analysis
Signal processing algorithms
Tools
Signal processing
Sampling methods
Radar polarimetry
Blue-noise
change detection
data fusion
graph
remote sensing images
sampling
smoothness
- Language
- ISSN
- 2153-7003
In this paper, we address the problem of sampling on graphs for change detection in large multi-spectral (MS) and synthetic aperture radar (SAR) images by proposing a graph-based data-driven framework. The main steps of the proposed approach are: (i) the segmentation of regions that enclose the change; (ii) the use of smoothness prior for learning a graph of the regions; (iii) the integration of blue-noise sampling (BN) in the change detection scheme. We validate our approach in 14 real cases of remote sensing according to quantitative analyses. The results confirm that using a structured sampling such as BN outperforms recent state-of-the-art methods in change detection for multimodal data.