Surface object recognition with CNN and SVM in Landsat 8 images

Tomohiro Ishii, Ryosuke Nakamura, Hidemoto Nakada, Yoshihiko Mochizuki, Hiroshi Ishikawa

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

20 Citations (Scopus)

Abstract

There is a series of earth observation satellites called Landsat, which send a very large amount of image data every day such that it is hard to analyze manually. Thus an effective application of machine learning techniques to automatically analyze such data is called for. In surface object recognition, which is one of the important applications of such data, the distribution of a specific object on the surface is surveyed. In this paper, we propose and compare two methods for surface object recognition, one using the convolutional neural network (CNN) and the other support vector machine (SVM). In our experiments, CNN showed higher performance than SVM. In addition, we observed that the number of negative samples have a influence on the performance, and it is necessary to select the number of them for practical use.

Original languageEnglish
Title of host publicationProceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages341-344
Number of pages4
ISBN (Print)9784901122153
DOIs
Publication statusPublished - 2015 Jul 8
Event14th IAPR International Conference on Machine Vision Applications, MVA 2015 - Tokyo, Japan
Duration: 2015 May 182015 May 22

Other

Other14th IAPR International Conference on Machine Vision Applications, MVA 2015
CountryJapan
CityTokyo
Period15/5/1815/5/22

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ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Ishii, T., Nakamura, R., Nakada, H., Mochizuki, Y., & Ishikawa, H. (2015). Surface object recognition with CNN and SVM in Landsat 8 images. In Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015 (pp. 341-344). [7153200] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MVA.2015.7153200