Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images

Yangyang Li; Guangyuan Liu; Tiantian Li; Licheng Jiao; Gao Lu; Naresh Marturi

doi:10.1109/ACCESS.2019.2962622

Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images

Yangyang Li, Guangyuan Liu^*, Tiantian Li, Licheng Jiao, Gao Lu, Naresh Marturi

^*Corresponding author for this work

Metallurgy and Materials

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

Data-driven optimization is an efficient global optimization algorithm for expensive black-box functions. In this paper, we apply data-driven optimization algorithm to the task of change detection with synthetic aperture radar (SAR) images for the first time. We first propose an easy-to-implement threshold algorithm for change detection in SAR images based on data-driven optimization. Its performance has been compared with commonly used methods like generalized Kittler and Illingworth threshold algorithms (GKIT). Next, we demonstrate how to tune the hyper-parameter of a (previously available) deep belief network (DBN) for change detection using data-driven optimization. Extensive evaluations are carried out using publicly available benchmark datasets. The obtained results suggest comparatively strong performance of our optimized DBN-based change detection algorithm.

Original language	English
Article number	8944046
Pages (from-to)	11426-11436
Number of pages	11
Journal	IEEE Access
Volume	8
DOIs	https://doi.org/10.1109/ACCESS.2019.2962622
Publication status	Published - 2020

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61772399, Grant U1701267, Grant 61773304, Grant 61672405, and Grant 61772400, in part by the Key Research and Development Program in Shaanxi Province of China under Grant 2019ZDLGY09-05, in part by the Program for Cheung Kong Scholars and Innovative Research Team in University under Grant IRT_15R53, in part by the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) under Grant B07048, and in part by the Technology Foundation for Selected Overseas Chinese Scholar in Shaanxi under Grant 2017021 and Grant 2018021.

Publisher Copyright:
© 2013 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

change detection
data-driven optimization
deep belief network (DBN)
Hyper-parameter optimization
synthetic aperture radar (SAR) image

ASJC Scopus subject areas

General Computer Science
General Materials Science
General Engineering

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10.1109/ACCESS.2019.2962622

Cite this

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title = "Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images",

abstract = "Data-driven optimization is an efficient global optimization algorithm for expensive black-box functions. In this paper, we apply data-driven optimization algorithm to the task of change detection with synthetic aperture radar (SAR) images for the first time. We first propose an easy-to-implement threshold algorithm for change detection in SAR images based on data-driven optimization. Its performance has been compared with commonly used methods like generalized Kittler and Illingworth threshold algorithms (GKIT). Next, we demonstrate how to tune the hyper-parameter of a (previously available) deep belief network (DBN) for change detection using data-driven optimization. Extensive evaluations are carried out using publicly available benchmark datasets. The obtained results suggest comparatively strong performance of our optimized DBN-based change detection algorithm.",

keywords = "change detection, data-driven optimization, deep belief network (DBN), Hyper-parameter optimization, synthetic aperture radar (SAR) image",

author = "Yangyang Li and Guangyuan Liu and Tiantian Li and Licheng Jiao and Gao Lu and Naresh Marturi",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61772399, Grant U1701267, Grant 61773304, Grant 61672405, and Grant 61772400, in part by the Key Research and Development Program in Shaanxi Province of China under Grant 2019ZDLGY09-05, in part by the Program for Cheung Kong Scholars and Innovative Research Team in University under Grant IRT_15R53, in part by the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) under Grant B07048, and in part by the Technology Foundation for Selected Overseas Chinese Scholar in Shaanxi under Grant 2017021 and Grant 2018021. Publisher Copyright: {\textcopyright} 2013 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2020",

doi = "10.1109/ACCESS.2019.2962622",

language = "English",

volume = "8",

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AU - Li, Yangyang

AU - Liu, Guangyuan

AU - Li, Tiantian

AU - Jiao, Licheng

AU - Lu, Gao

AU - Marturi, Naresh

N1 - Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61772399, Grant U1701267, Grant 61773304, Grant 61672405, and Grant 61772400, in part by the Key Research and Development Program in Shaanxi Province of China under Grant 2019ZDLGY09-05, in part by the Program for Cheung Kong Scholars and Innovative Research Team in University under Grant IRT_15R53, in part by the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) under Grant B07048, and in part by the Technology Foundation for Selected Overseas Chinese Scholar in Shaanxi under Grant 2017021 and Grant 2018021. Publisher Copyright: © 2013 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - Data-driven optimization is an efficient global optimization algorithm for expensive black-box functions. In this paper, we apply data-driven optimization algorithm to the task of change detection with synthetic aperture radar (SAR) images for the first time. We first propose an easy-to-implement threshold algorithm for change detection in SAR images based on data-driven optimization. Its performance has been compared with commonly used methods like generalized Kittler and Illingworth threshold algorithms (GKIT). Next, we demonstrate how to tune the hyper-parameter of a (previously available) deep belief network (DBN) for change detection using data-driven optimization. Extensive evaluations are carried out using publicly available benchmark datasets. The obtained results suggest comparatively strong performance of our optimized DBN-based change detection algorithm.

AB - Data-driven optimization is an efficient global optimization algorithm for expensive black-box functions. In this paper, we apply data-driven optimization algorithm to the task of change detection with synthetic aperture radar (SAR) images for the first time. We first propose an easy-to-implement threshold algorithm for change detection in SAR images based on data-driven optimization. Its performance has been compared with commonly used methods like generalized Kittler and Illingworth threshold algorithms (GKIT). Next, we demonstrate how to tune the hyper-parameter of a (previously available) deep belief network (DBN) for change detection using data-driven optimization. Extensive evaluations are carried out using publicly available benchmark datasets. The obtained results suggest comparatively strong performance of our optimized DBN-based change detection algorithm.

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Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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