Study on Urban Heat Island Intensity Level Identification Based on an Improved Restricted Boltzmann Machine

Yang Zhang, Ping Jiang, Hongyan Zhang and Peng Cheng
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Yang Zhang: College of Urban Economics and Public Administration, Capital University of Economics and Business, Beijing 100070, China
Ping Jiang: College of Resources and Environmental Science, Wuhan University, Wuhan 430079, China
Hongyan Zhang: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China
Peng Cheng: College of Public Administration, Guangxi University, Nanning 530004, China

IJERPH, 2018, vol. 15, issue 2, 1-16

Abstract: Thermal infrared remote sensing has become one of the main technology methods used for urban heat island research. When applying urban land surface temperature inversion of the thermal infrared band, problems with intensity level division arise because the method is subjective. However, this method is one of the few that performs heat island intensity level identification. This paper will build an intensity level identifier for an urban heat island, by using weak supervision and thought-based learning in an improved, restricted Boltzmann machine (RBM) model. The identifier automatically initializes the annotation and optimizes the model parameters sequentially until the target identifier is completed. The algorithm needs very little information about the weak labeling of the target training sample and generates an urban heat island intensity spatial distribution map. This study can provide reliable decision-making support for urban ecological planning and effective protection of urban ecological security. The experimental results showed the following: (1) The heat island effect in Wuhan is existent and intense. Heat island areas are widely distributed. The largest heat island area is in Wuhan, followed by the sub-green island. The total area encompassed by heat island and strong island levels accounts for 54.16% of the land in Wuhan. (2) Partially based on improved RBM identification, this method meets the research demands of determining the spatial distribution characteristics of the internal heat island effect; its identification accuracy is superior to that of comparable methods.

Keywords: improved restricted Boltzmann machine; urban heat island; intensity level identification; green island; Wuhan; China (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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