Next Article in Journal
Multi-Decadal Surface Water Dynamics in North American Tundra
Next Article in Special Issue
Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images
Previous Article in Journal
Performance of MODIS C6 Aerosol Product during Frequent Haze-Fog Events: A Case Study of Beijing
Previous Article in Special Issue
Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(5), 498; doi:10.3390/rs9050498

Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network

1
Department Of Engineering Physics, Tsinghua University, Beijing 100084, China
2
China Institute of Water Resources and Hydropower Research (IWHR), Beijing 100038, China
3
Beijing Soil and Water Conservation Center, Beijing 100036, China
4
Water Resources Information Center of Henan Province, Zhengzhou 450003, China
*
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Lenio Soares Galvao and Prasad S. Thenkabail
Received: 21 March 2017 / Revised: 13 May 2017 / Accepted: 16 May 2017 / Published: 18 May 2017
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
View Full-Text   |   Download PDF [10169 KB, uploaded 18 May 2017]   |  

Abstract

As a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully Convolutional Network (FCN) model achieved state-of-the-art performance for natural image semantic segmentation. In this paper, an accurate classification approach for high resolution remote sensing imagery based on the improved FCN model is proposed. Firstly, we improve the density of output class maps by introducing Atrous convolution, and secondly, we design a multi-scale network architecture by adding a skip-layer structure to make it capable for multi-resolution image classification. Finally, we further refine the output class map using Conditional Random Fields (CRFs) post-processing. Our classification model is trained on 70 GF-2 true color images, and tested on the other 4 GF-2 images and 3 IKONOS true color images. We also employ object-oriented classification, patch-based CNN classification, and the FCN-8s approach on the same images for comparison. The experiments show that compared with the existing approaches, our approach has an obvious improvement in accuracy. The average precision, recall, and Kappa coefficient of our approach are 0.81, 0.78, and 0.83, respectively. The experiments also prove that our approach has strong applicability for multi-resolution image classification. View Full-Text
Keywords: deep learning; convolutional neural network (CNN); fully convolutional network (FCN); classification; remote sensing; high resolution deep learning; convolutional neural network (CNN); fully convolutional network (FCN); classification; remote sensing; high resolution
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

Fu, G.; Liu, C.; Zhou, R.; Sun, T.; Zhang, Q. Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network. Remote Sens. 2017, 9, 498.

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