BLUE NOISE SAMPLING AND NYSTRÖM EXTENSION FOR GRAPH BASED CHANGE DETECTION

David Alejandro Jimenez-Sierra; Hernán Darío Benítez-Restrepo; Gonzalo R. Arce; Juan F. Florez-Ospina

doi:10.1109/IGARSS47720.2021.9555107

BLUE NOISE SAMPLING AND NYSTRÖM EXTENSION FOR GRAPH BASED CHANGE DETECTION

David Alejandro Jimenez-Sierra, Hernán Darío Benítez-Restrepo, Gonzalo R. Arce, Juan F. Florez-Ospina

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

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 graphbased 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.

Original language	English
Pages	2895-2898
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9555107
State	Published - 2021
Event	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021

Conference

Conference	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/Territory	Belgium
City	Brussels
Period	12/07/21 → 16/07/21

Keywords

Blue-noise
change detection
data fusion
graph
remote sensing images
sampling
smoothness

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10.1109/IGARSS47720.2021.9555107

Cite this

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title = "BLUE NOISE SAMPLING AND NYSTR{\"O}M EXTENSION FOR GRAPH BASED CHANGE DETECTION",

abstract = "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 graphbased 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.",

keywords = "Blue-noise, change detection, data fusion, graph, remote sensing images, sampling, smoothness",

author = "Jimenez-Sierra, {David Alejandro} and Ben{\'i}tez-Restrepo, {Hern{\'a}n Dar{\'i}o} and Arce, {Gonzalo R.} and Florez-Ospina, {Juan F.}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",

year = "2021",

doi = "10.1109/IGARSS47720.2021.9555107",

language = "English",

pages = "2895--2898",

}

BLUE NOISE SAMPLING AND NYSTRÖM EXTENSION FOR GRAPH BASED CHANGE DETECTION. / Jimenez-Sierra, David Alejandro; Benítez-Restrepo, Hernán Darío; Arce, Gonzalo R. et al.
2021. 2895-2898 Paper presented at 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels, Belgium.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - BLUE NOISE SAMPLING AND NYSTRÖM EXTENSION FOR GRAPH BASED CHANGE DETECTION

AU - Jimenez-Sierra, David Alejandro

AU - Benítez-Restrepo, Hernán Darío

AU - Arce, Gonzalo R.

AU - Florez-Ospina, Juan F.

PY - 2021

Y1 - 2021

N2 - 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 graphbased 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.

AB - 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 graphbased 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.

KW - Blue-noise

KW - change detection

KW - data fusion

KW - graph

KW - remote sensing images

KW - sampling

KW - smoothness

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DO - 10.1109/IGARSS47720.2021.9555107

M3 - Paper

AN - SCOPUS:85129884845

SP - 2895

EP - 2898

T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

Y2 - 12 July 2021 through 16 July 2021

ER -

BLUE NOISE SAMPLING AND NYSTRÖM EXTENSION FOR GRAPH BASED CHANGE DETECTION

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Keywords

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