Next Article in Journal
Performance of MODIS C6 Aerosol Product during Frequent Haze-Fog Events: A Case Study of Beijing
Next Article in Special Issue
Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network
Previous Article in Journal / Special Issue
Multi-Scale Analysis of Very High Resolution Satellite Images Using Unsupervised Techniques
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(5), 494; doi:10.3390/rs9050494

Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

1
Institute of Automation Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Science, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Gonzalo Pajares Martinsanz, Xiaofeng Li and Prasad S. Thenkabail
Received: 26 February 2017 / Revised: 28 April 2017 / Accepted: 15 May 2017 / Published: 18 May 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)

Abstract

Joint vehicle localization and categorization in high resolution aerial images can provide useful information for applications such as traffic flow structure analysis. To maintain sufficient features to recognize small-scaled vehicles, a regions with convolutional neural network features (R-CNN) -like detection structure is employed. In this setting, cascaded localization error can be averted by equally treating the negatives and differently typed positives as a multi-class classification task, but the problem of class-imbalance remains. To address this issue, a cost-effective network extension scheme is proposed. In it, the correlated convolution and connection costs during extension are reduced by feature map selection and bi-partite main-side network construction, which are realized with the assistance of a novel feature map class-importance measurement and a new class-imbalance sensitive main-side loss function. By using an image classification dataset established from a set of traditional real-colored aerial images with 0.13 m ground sampling distance which are taken from the height of 1000 m by an imaging system composed of non-metric cameras, the effectiveness of the proposed network extension is verified by comparing with its similarly shaped strong counter-parts. Experiments show an equivalent or better performance, while requiring the least parameter and memory overheads are required. View Full-Text
Keywords: vehicle localization; vehicle classification; high resolution; aerial image; convolutional neural network (CNN); class imbalance vehicle localization; vehicle classification; high resolution; aerial image; convolutional neural network (CNN); class imbalance
Figures

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).

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

Li, F.; Li, S.; Zhu, C.; Lan, X.; Chang, H. Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images. Remote Sens. 2017, 9, 494.

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]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top