Automatic Extraction and Classication of Vegetation Areas from High Resolution Images in Urban Areas

Corina Iovan 1 Didier Boldo Matthieu Cord 1 Mats Erikson
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This paper presents a complete high resolution aerial-images processing workflow to detect and characterize vegetation structures in high density urban areas. We present a hierarchical strategy to extract, analyze and delineate vegetation areas according to their height. To detect urban vegetation areas, we develop two methods, one using spectral indices and the second one based on a Support Vector Machines (SVM) classifier. Once vegetation areas detected, we differentiate lawns from treed areas by computing a texture operator on the Digital Surface Model (DSM). A robust region growing method based on the DSM is proposed for an accurate delineation of tree crowns. Delineation results are compared to results obtained by a Random Walk region growing technique for tree crown delineation. We evaluate the accuracy of the tree crown delineation results to a reference manual delineation. Results obtained are discussed and the influential factors are put forward.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01336456
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Submitted on : Thursday, June 23, 2016 - 10:41:26 AM
Last modification on : Thursday, March 21, 2019 - 1:05:21 PM

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Corina Iovan, Didier Boldo, Matthieu Cord, Mats Erikson. Automatic Extraction and Classication of Vegetation Areas from High Resolution Images in Urban Areas. Scandinavian Conference on Artificial Intelligence, Jun 2007, Aalborg, Denmark. pp.858-867, ⟨10.1007/978-3-540-73040-8_87⟩. ⟨hal-01336456⟩

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