APPLICATION OF MACHINE LEARNING IN URBAN GREENERY LAND COVER EXTRACTION

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Li, L. L.^2¹  Qiao, X.^1² 

[1] College of Information Science and Engineering, Ocean University of China, Songling Road, Qingdao, China^2;Qingdao Geotechnical Investigation and Surveying Institute, State and Local Joint Engineering Research Center for the Integration and Application of Sea-land Geographical Information , Shandong Road, Qingdao, China^1

关键词: Neural Network;    Machine Learning;    Greenery Land Cover;    Auto Extraction;    Multispectral Image;   

DOI  :  10.5194/isprs-archives-XLII-3-1409-2018

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

Urban greenery is a critical part of the modern city and the greenery coverage information is essential for land resource management, environmental monitoring and urban planning. It is a challenging work to extract the urban greenery information from remote sensing image as the trees and grassland are mixed with city built-ups. In this paper, we propose a new automatic pixel-based greenery extraction method using multispectral remote sensing images. The method includes three main steps. First, a small part of the images is manually interpreted to provide prior knowledge. Secondly, a five-layer neural network is trained and optimised with the manual extraction results, which are divided to serve as training samples, verification samples and testing samples. Lastly, the well-trained neural network will be applied to the unlabelled data to perform the greenery extraction. The GF-2 and GJ-1 high resolution multispectral remote sensing images were used to extract greenery coverage information in the built-up areas of city X. It shows a favourable performance in the 619 square kilometers areas. Also, when comparing with the traditional NDVI method, the proposed method gives a more accurate delineation of the greenery region. Due to the advantage of low computational load and high accuracy, it has a great potential for large area greenery auto extraction, which saves a lot of manpower and resources.

【 授权许可】

CC BY