Sensors, Vol. 18, Pages 4333: Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images

Sensors, Vol. 18, Pages 4333: Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images Sensors doi: 10.3390/s18124333 Authors: Poliyapram Vinayaraj Nevrez Imamoglu Ryosuke Nakamura Atsushi Oda 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. Mo...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research