Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 553014
Title Super-resolution land cover mapping based on the convolutional neural network
Author(s) Jia, Yuanxin; Ge, Yong; Chen, Yuehong; Li, Sanping; Heuvelink, Gerard B.M.; Ling, Feng
Source Remote Sensing 11 (2019)15. - ISSN 2072-4292
DOI https://doi.org/10.3390/rs11151815
Department(s) Soil Geography and Landscape
PE&RC
ISRIC - World Soil Information
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Convolutional neural network - Land cover - Remote sensing imagery - Super-resolution mapping
Abstract

Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spatial characteristics of geographic objects and extrapolating calibrated methods to other study areas, it may be a useful approach to overcome limitations of current SRM methods. In this paper, a new SRM method based on the CNN (SRMCNN) is proposed and tested. Specifically, an encoder-decoder CNN is used to model the nonlinear relationship between coarse remote sensing images and fine-scale land cover maps. Two real-image experiments were conducted to analyze the effectiveness of the proposed method. The results demonstrate that the overall accuracy of the proposed SRMCNN method was 3% to 5% higher than that of two existing SRM methods. Moreover, the proposed SRMCNN method was validated by visualizing output features and analyzing the performance of different geographic objects.

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