Multi-Scale Convolutional Neural Network for Remote Sensing Scene Classification

Haikel Alhichri, Naif Alajlan, Yakoub Bazi, Timon Rabczuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In recent years the problem of scene classification in remote sensing has attracted a considerable amount of attention. Solution for this important problem based on deep convolutional neural networks (CNN) are currently state-of-the-art. So far all CNNs used images of fixed size (typically 224× 224 which commonly used in other fields of computer vision). In this paper, we propose a multi-scale deep CNN architecture that can accept variable image sizes. We achieve this by using multiple CNN, that share some or all parameters, followed by a merge layer, fully connected layers, and finally a softmax layer for classification. In each epoch we train the network with a batch of images of all scales. We have implemented this architecture using three SqueezeNet CNNs trained on three different scales of scene images. Then we carried out experiments on three well know datasets, namely UC Merced, KSA, and AID datasets. Preliminary results show that this multi-scale CNN do converge just as the traditional single-scale training, and leads to better testing accuracy.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Electro/Information Technology, EIT 2018
PublisherIEEE Computer Society
Pages113-117
Number of pages5
Volume2018-May
ISBN (Electronic)9781538653982
DOIs
Publication statusPublished - 2018 Oct 18
Externally publishedYes
Event2018 IEEE International Conference on Electro/Information Technology, EIT 2018 - Rochester, United States
Duration: 2018 May 32018 May 5

Other

Other2018 IEEE International Conference on Electro/Information Technology, EIT 2018
CountryUnited States
CityRochester
Period18/5/318/5/5

Keywords

  • Convolutional neural networks (CNN)
  • Deep learning
  • Deep neural networks
  • Remote sensing
  • Scene classification

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Alhichri, H., Alajlan, N., Bazi, Y., & Rabczuk, T. (2018). Multi-Scale Convolutional Neural Network for Remote Sensing Scene Classification. In 2018 IEEE International Conference on Electro/Information Technology, EIT 2018 (Vol. 2018-May, pp. 113-117). [8500107] IEEE Computer Society. https://doi.org/10.1109/EIT.2018.8500107