Spatial Attraction Models Coupled with Elman Neural Networks for Enhancing Sub-Pixel Urban Inundation Mapping

Lingkui Meng,Tingbao Xu,Linyi Li,Chang Huang,Yun Chen,Kaifang Shi

doi:10.3390/rs12132068

Abstract

Urban flooding is one of the most costly and destructive natural hazards worldwide. Remote-sensing images with high temporal resolutions have been extensively applied to timely inundation monitoring, assessing and mapping, but are limited by their low spatial resolution. Sub-pixel mapping has drawn great attention among researchers worldwide and has demonstrated a promising potential of high-accuracy mapping of inundation. Aimed to boost sub-pixel urban inundation mapping (SUIM) from remote-sensing imagery, a new algorithm based on spatial attraction models and Elman neural networks (SAMENN) was developed and examined in this paper. The Elman neural networks (ENN)-based SUIM module was developed firstly. Then a normalized edge intensity index of mixed pixels was generated. Finally the algorithm of SAMENN-SUIM was constructed and implemented. Landsat 8 images of two cities of China, which experienced heavy floods, were used in the experiments. Compared to three traditional SUIM methods, SAMENN-SUIM attained higher mapping accuracy according not only to visual evaluations but also quantitative assessments. The effects of normalized edge intensity index threshold and neuron number of the hidden layer on accuracy of the SAMENN-SUIM algorithm were analyzed and discussed. The newly developed algorithm in this study made a positive contribution to advancing urban inundation mapping from remote-sensing images with medium-low spatial resolutions, and hence can favor urban flood monitoring and risk assessment.

Highlights

Urban flooding is one of the most costly and destructive natural hazards worldwide, which poses a great threat to urban economic development and human safety [1,2]
Numerous methods and improvements have been established and acquired, such as approaches based on genetic algorithm [11], spatial attraction models (SAM) [12,13], optimal endmember [14], spectral-spatial model [15], support vector machine (SVM) [16] and artificial neural networks [16,17,18]
For BPNN-Sub-pixel urban inundation mapping (SUIM), SVM-SUIM and spatial attraction models and Elman neural networks (SAMENN)-SUIM, we randomly chose 20% of the mixed pixels as the training samples

Summary

Introduction

Urban flooding is one of the most costly and destructive natural hazards worldwide, which poses a great threat to urban economic development and human safety [1,2]. Urban inundation mapping, which can obtain inundation distribution information for flood monitoring and risk assessment [4,5,6,7], has become increasingly important. High temporal resolution remote-sensing images have been extensively applied to timely inundation mapping and monitoring in recent years [8,9,10]. The mapping accuracy of urban inundation is substantially compromised due to the low spatial resolutions of such images, which in a certain degree constrains the application of those valuable high temporal resolution remote-sensing data in flooding inundation mapping. Sub-pixel urban inundation mapping (SUIM) can acquire the spatial distribution of urban inundation at a sub-pixel scale by detailing the spatial information structure inside a pixel to, boost the accuracy of inundation mapping. SUIM in urban environments has been retained as a challenging research topic, because ground targets in urban remote-sensing images are complex

Methods

Results

Discussion

Conclusion