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Muhammad Rizwan Asif

An efficient region proposal method for optical remote sensing imagery

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

  • Shahid Karim, Harbin Institute of Technology
  • ,
  • Ye Zhang, Harbin Institute of Technology
  • ,
  • Shoulin Yin, Harbin Institute of Technology
  • ,
  • Muhammad Rizwan Asif

Region proposals are very important for several perfections of remote sensing applications such as vehicle detection, traffic surveillance and intelligent transport system. In this paper, an efficient region proposal approach has been proposed. The framework is organized into two key steps. The first step is based on extracting region proposals using Cascade system. The second step is based on the classification of extracted region proposals which is performed by transfer learning using Convolutional Neural Networks (CNN) and AlexNet architecture is utilized for transfer learning. The aim of this investigation is to evaluate the proposed method for vehicle detection. The selective search (SS) method is also briefly discussed for comparison. The results regarding vehicle detection are very promising.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Number of pages4
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication year31 Oct 2018
Article number8518098
ISBN (Electronic)9781538671504
Publication statusPublished - 31 Oct 2018
Externally publishedYes
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018


Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
SponsorGeoscience and Remote Sensing Society (GRSS), The Institute of Electrical and Electronics Engineers (IEEE)
SeriesInternational Geoscience and Remote Sensing Symposium (IGARSS)

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China under Grants 61471148.

Publisher Copyright:
© 2018 IEEE

    Research areas

  • AlexNet, Cascade, Classification, Region proposal, Selective search, Transfer learning

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