Next Article in Journal
Acknowledgement to Reviewers of Algorithms in 2017
Next Article in Special Issue
Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion
Previous Article in Journal
On Application of the Ray-Shooting Method for LQR via Static-Output-Feedback
Article Menu

Export Article

Open AccessArticle
Algorithms 2018, 11(1), 9; doi:10.3390/a11010009

Application of a Hybrid Model Based on a Convolutional Auto-Encoder and Convolutional Neural Network in Object-Oriented Remote Sensing Classification

Resource & Environment Engineering College, Wuhan University of Technology, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Received: 5 December 2017 / Revised: 8 January 2018 / Accepted: 15 January 2018 / Published: 16 January 2018
(This article belongs to the Special Issue Advanced Artificial Neural Networks)
View Full-Text   |   Download PDF [3202 KB, uploaded 16 January 2018]   |  

Abstract

Variation in the format and classification requirements for remote sensing data makes establishing a standard remote sensing sample dataset difficult. As a result, few remote sensing deep neural network models have been widely accepted. We propose a hybrid deep neural network model based on a convolutional auto-encoder and a complementary convolutional neural network to solve this problem. The convolutional auto-encoder supports feature extraction and data dimension reduction of remote sensing data. The extracted features are input into the convolutional neural network and subsequently classified. Experimental results show that in the proposed model, the classification accuracy increases from 0.916 to 0.944, compared to a traditional convolutional neural network model; furthermore, the number of training runs is reduced from 40,000 to 22,000, and the number of labelled samples can be reduced by more than half, all while ensuring a classification accuracy of no less than 0.9, which suggests the effectiveness and feasibility of the proposed model. View Full-Text
Keywords: remote sensing classification; object-oriented; convolutional auto-encoder; convolutional neural network remote sensing classification; object-oriented; convolutional auto-encoder; convolutional neural network
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Cui, W.; Zhou, Q.; Zheng, Z. Application of a Hybrid Model Based on a Convolutional Auto-Encoder and Convolutional Neural Network in Object-Oriented Remote Sensing Classification. Algorithms 2018, 11, 9.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top