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Proceedings Paper

Machine learning methods and classification of vegetation in Brest, France
Author(s): Guanyao Xie; Simona Niculescu; Chinguyen Lam; Elise Seveno
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Paper Abstract

Urban green land plays a special significant role in our life. Many previous studies have already proven the feasibility of satellite imagery processing for urban green land detection, and many classification techniques were tested for this purpose. In this paper, two methods of Machine learning, such as Artificial Neural Networks (ANN) and Random Forest (RF) were tested on a series of very high spatial resolution satellite imagery to classify, highlight urban green lands and eventually study their change in three years. Firstly, we present Multilayer Perceptron (MLP), a simple feed-forward neural network which consists of two steps, SLIC superpixel segmentation and image classification through MLP trained model. Secondly, RF technique was applied and compared to the previous classification. Then the classification results were interpreted at the final stage. These methods are performed on satellite images Pléiades of the city of Brest (3 images acquired in 2016, 2017 and 2018), these multispectral images are already preprocessed and all pan-sharpened to 50 cm spatial resolution.

Paper Details

Date Published: 2 October 2019
PDF: 10 pages
Proc. SPIE 11157, Remote Sensing Technologies and Applications in Urban Environments IV, 111570J (2 October 2019);
Show Author Affiliations
Guanyao Xie, Lab. LETG-Brest, Géomer, CNRS, IUEM-UBO (France)
Simona Niculescu, Lab. LETG-Brest, Géomer, CNRS, IUEM-UBO (France)
Chinguyen Lam, Lab. LETG-Brest, Géomer, CNRS, IUEM-UBO (France)
Elise Seveno, Lab. LETG-Brest, Géomer, CNRS, IUEM-UBO (France)


Published in SPIE Proceedings Vol. 11157:
Remote Sensing Technologies and Applications in Urban Environments IV
Thilo Erbertseder; Nektarios Chrysoulakis; Ying Zhang; Frank Baier, Editor(s)

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