A Method for Fast Remote Sensing Images Classification

Fengjiao Li, Donglin Jing, Fukun Bi, Hao Shi

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

Abstract

Due to the characteristic of high score remote sensing images, such as large file, chaotic content and complex information, the demand for image classification speed and accuracy is increasing. In this paper, we combine the convolutional neural network and octave convolution to extract the complex high-order statistical properties of the image. We first analyze the more classical convolutional neural network, and use the octave convolution to make full use of the fine details of the changes of the high-frequency information of the remote sensing image. The memory and computing resources consumed are greatly reduced, and network optimization is performed. The test proves that the result is effective for speed and accuracy improvements.

Original languageEnglish
Title of host publicationICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728123455
DOIs
Publication statusPublished - Dec 2019
Event2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019 - Chongqing, China
Duration: 11 Dec 201913 Dec 2019

Publication series

NameICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019

Conference

Conference2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Country/TerritoryChina
CityChongqing
Period11/12/1913/12/19

Keywords

  • Convolutional neural network
  • Image frequency
  • Octave convolution
  • Remote sensing image

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