Remote Sens. 2014, 6(5), 3554-3582; doi:10.3390/rs6053554
Article

Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

1,* email, 2email, 1email and 1email
Received: 20 February 2014; in revised form: 7 April 2014 / Accepted: 16 April 2014 / Published: 25 April 2014
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.
Abstract: Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages.
Keywords: object-based classification; greenhouses; GeoEye-1; WorldView-2; normalized digital surface model; multiangle image
PDF Full-text Download PDF Full-Text [4231 KB, uploaded 19 June 2014 02:21 CEST]

Export to BibTeX |
EndNote


MDPI and ACS Style

Aguilar, M.A.; Bianconi, F.; Aguilar, F.J.; Fernández, I. Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery. Remote Sens. 2014, 6, 3554-3582.

AMA Style

Aguilar MA, Bianconi F, Aguilar FJ, Fernández I. Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery. Remote Sensing. 2014; 6(5):3554-3582.

Chicago/Turabian Style

Aguilar, Manuel A.; Bianconi, Francesco; Aguilar, Fernando J.; Fernández, Ismael. 2014. "Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery." Remote Sens. 6, no. 5: 3554-3582.

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