Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1409-1413, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1409-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1409-1413, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1409-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

APPLICATION OF MACHINE LEARNING IN URBAN GREENERY LAND COVER EXTRACTION

X. Qiao1, L. L. Li2, D. Li1, Y. L. Gan1, and A. Y. Hou1 X. Qiao et al.
  • 1Qingdao 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
  • 2College of Information Science and Engineering, Ocean University of China, Songling Road, Qingdao, China

Keywords: Neural Network, Machine Learning, Greenery Land Cover, Auto Extraction, Multispectral Image

Abstract. 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.