Enhanced Super-Resolution Mapping of Urban Floods Based on the Fusion of Support Vector Machine and General Regression Neural Network

Linyi Li*, Yun Chen, Tingbao Xu, Kaifang Shi, Chang Huang, Rui Liu, Binbin Lu, Lingkui Meng

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)

    Abstract

    Super-resolution mapping of urban flood (SMUF) is one of the hotspots in remote sensing and urban environment research. In this letter, a new SMUF method based on the fusion of support vector machine and general regression neural network (FSVMGRNN) was proposed to achieve enhanced performance. An SVM-SMUF algorithm was developed and a fusion criterion was formulated. Then, the FSVMGRNN-SMUF algorithm was developed. The results of FSVMGRNN-SMUF were evaluated using Landsat 8 OLI imagery of two representative cities in China. FSVMGRNN-SMUF yielded the most accurate SMUF results among the five SMUF methods according to visual comparisons and quantitative comparisons. The mapping accuracy of FSVMGRNN-SMUF related to the kernel functions was also analyzed and discussed. The results of this letter will help to boost practical applications of median-low resolution remote sensing images in urban flooding mapping, and to strengthen the means for monitoring and assessing urban flooding disasters.

    Original languageEnglish
    Article number8636408
    Pages (from-to)1269-1273
    Number of pages5
    JournalIEEE Geoscience and Remote Sensing Letters
    Volume16
    Issue number8
    DOIs
    Publication statusPublished - Aug 2019

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