Very high resolution images classification by fine tuning deep convolutional neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The analysis and interpretation of satellite images generally require the realization of a classification step. For this purpose, many methods over the year have been developed with good performances. But with the explosion of VHR images availability, these methods became more difficult to use. Recently, deep neural networks emerged as a method to address the VHR images classification which is a key point in remote sensing field. This work aims to evaluate the performance of fine-tuning pretrained convolutional neural networks (CNNs) on the classification of VHR imagery. The results are promising since they show better accuracy comparing to that of CNNs as features extractor.

Original languageEnglish
Title of host publicationEighth International Conference on Digital Image Processing, ICDIP 2016
EditorsXudong Jiang, Charles M. Falco
PublisherSPIE
ISBN (Electronic)9781510605039
DOIs
StatePublished - 2016
Event8th International Conference on Digital Image Processing, ICDIP 2016 - Chengu, China
Duration: 20 May 201623 May 2016

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10033
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference8th International Conference on Digital Image Processing, ICDIP 2016
Country/TerritoryChina
CityChengu
Period20/05/1623/05/16

Keywords

  • Classification
  • Convolutional neural networks
  • Fine-tuning
  • VHR images

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