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Sensors 2018, 18(12), 4333; https://doi.org/10.3390/s18124333

Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images

1
AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), Tokyo 152-8550, Japan
2
National Institute of Advanced Industrial Technology (AIST), Tokyo 135-0064, Japan
*
Author to whom correspondence should be addressed.
Received: 27 September 2018 / Revised: 28 November 2018 / Accepted: 4 December 2018 / Published: 7 December 2018
(This article belongs to the Special Issue Applications of Remote Sensing Data in Water Resources Management)
PDF [9951 KB, uploaded 7 December 2018]

Abstract

Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches—Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network—using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.
Keywords: AWEI; deep neural network; Landsat-8; MNDWI; PDWF; perceptron neural network; surface water bodies AWEI; deep neural network; Landsat-8; MNDWI; PDWF; perceptron neural network; surface water bodies
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Vinayaraj, P.; Imamoglu, N.; Nakamura, R.; Oda, A. Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images. Sensors 2018, 18, 4333.

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