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

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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 languageEnglish
Article number8944046
Pages (from-to)11426-11436
Number of pages11
JournalIEEE Access
Publication statusPublished - 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 2020 Elsevier B.V., All rights reserved.


  • 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


Dive into the research topics of 'Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images'. Together they form a unique fingerprint.

Cite this