Detection, segmentation and characterisation of vegetation in high-resolution aerial images for 3d city modelling

Corina Iovan 1 Didier Boldo Matthieu Cord 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : An approach for tree species classification in urban areas from high resolution colour infrared (CIR) aerial images and the corresponding Digital Surface Model (DSM) is described in this paper. The proposed method is a supervised classification one based on a Support Vector Machines (SVM) classifier. Texture features from the Gray Level Co-occurrence Matrix (GLCM) are computed to form feature vectors for both per-pixel and per-region classification approaches. The two approaches are presented and results obtained are evaluated and compared both against each other and also against a manual defined ground truth. To perform tree species classification on high-density urban area images, trees must previously be segmented into individual objects. All intermediary methods developed to segment individual trees will also be shortly described. Tree parameters (height, crown diameter) are estimated from the DSM. These parameters together with the tree species information are used for a 3D realistic modelling of the trees in urban environments. Results of the described system are presented for a typical scene.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01301579
Contributor : Lip6 Publications <>
Submitted on : Tuesday, April 12, 2016 - 2:48:40 PM
Last modification on : Thursday, March 21, 2019 - 1:09:08 PM

Identifiers

  • HAL Id : hal-01301579, version 1

Citation

Corina Iovan, Didier Boldo, Matthieu Cord. Detection, segmentation and characterisation of vegetation in high-resolution aerial images for 3d city modelling. XXI ISPRS Congress, Jul 2008, Beijin, China. pp.247-252. ⟨hal-01301579⟩

Share

Metrics

Record views

71