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

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

1
Department of Engineering, University of Almería, Ctra. de Sacramento s/n, La Cañada de San Urbano, E-04120 Almería, Spain
2
Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
*
Author to whom correspondence should be addressed.
Received: 20 February 2014 / Revised: 7 April 2014 / Accepted: 16 April 2014 / Published: 25 April 2014
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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. View Full-Text
Keywords: object-based classification; greenhouses; GeoEye-1; WorldView-2; normalized digital surface model; multiangle image object-based classification; greenhouses; GeoEye-1; WorldView-2; normalized digital surface model; multiangle image
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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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.

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