Next Article in Journal
Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection
Next Article in Special Issue
Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery
Previous Article in Journal
Supervised Sub-Pixel Mapping for Change Detection from Remotely Sensed Images with Different Resolutions
Previous Article in Special Issue
UAV-Based Oblique Photogrammetry for Outdoor Data Acquisition and Offsite Visual Inspection of Transmission Line
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(3), 285; doi:10.3390/rs9030285

Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler’s First Law of Geography for Very High Resolution Aerial Imagery Classification

1
School of Computer Science and Engineering, Xi’An University of Technology, Xi’an 710048, China
2
School of Remote Sensing and Engineering Information, Wuhan University, Wuhan 430072, China
3
Collaborative Innovation Center of Geospatial Information Technology, Wuhan University, Wuhan 430079, China
4
Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik IS 107, Iceland
*
Authors to whom correspondence should be addressed.
Academic Editors: Farid Melgani, Francesco Nex, Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 1 November 2016 / Revised: 18 February 2017 / Accepted: 12 March 2017 / Published: 17 March 2017
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
View Full-Text   |   Download PDF [9408 KB, uploaded 17 March 2017]   |  

Abstract

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification. View Full-Text
Keywords: spatial-spectral feature; very high spatial resolution image; classification; Tobler’s First Law of Geography spatial-spectral feature; very high spatial resolution image; classification; Tobler’s First Law of Geography
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

Lv, Z.; Zhang, P.; Atli Benediktsson, J. Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler’s First Law of Geography for Very High Resolution Aerial Imagery Classification. Remote Sens. 2017, 9, 285.

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