Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery
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 ar...
| Main Authors: | , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/7370 |
| _version_ | 1789406623712673792 |
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| author | Aguilar Torres, Manuel Ángel Bianconi Pettirossi, Francesco Aguilar Torres, Fernando José Fernández Luque, Ismael |
| author_facet | Aguilar Torres, Manuel Ángel Bianconi Pettirossi, Francesco Aguilar Torres, Fernando José Fernández Luque, Ismael |
| author_sort | Aguilar Torres, Manuel Ángel |
| collection | DSpace |
| description | 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. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7370 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-73702023-10-10T11:07:37Z Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery Aguilar Torres, Manuel Ángel Bianconi Pettirossi, Francesco Aguilar Torres, Fernando José Fernández Luque, Ismael object-based classification greenhouses GeoEye-1 WorldView-2 normalized digital surface model multiangle image 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. 2020-01-16T11:03:46Z 2020-01-16T11:03:46Z 2014-04-25 info:eu-repo/semantics/article 2072-4292 http://hdl.handle.net/10835/7370 en https://www.mdpi.com/2072-4292/6/5/3554 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | object-based classification greenhouses GeoEye-1 WorldView-2 normalized digital surface model multiangle image Aguilar Torres, Manuel Ángel Bianconi Pettirossi, Francesco Aguilar Torres, Fernando José Fernández Luque, Ismael Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery |
| title | Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery |
| title_full | Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery |
| title_fullStr | Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery |
| title_full_unstemmed | Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery |
| title_short | Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery |
| title_sort | object-based greenhouse classification from geoeye-1 and worldview-2 stereo imagery |
| topic | object-based classification greenhouses GeoEye-1 WorldView-2 normalized digital surface model multiangle image |
| url | http://hdl.handle.net/10835/7370 |
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