Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

作者：Wei Jiang, Guojin He, Tengfei Long, Yuan Ni, Huichan Liu, Yan Peng, Kenan Lv, Guizhou Wang · 发表于：Remote Sensing · 年份：2018 · DOI：10.3390/rs10050755 · 被引用次数：123 · 研究领域：Flood Risk Assessment and Management、Hydrological Forecasting Using AI、Hydrology and Watershed Management Studies

Surface water mapping is essential for monitoring climate change, water resources, ecosystem services and the hydrological cycle. In this study, we adopt a multilayer perceptron (MLP) neural network to identify surface water in Landsat 8 satellite images. To evaluate the performance of the proposed method when extracting surface water, eight images of typical regions are collected, and a water index and support vector machine are employed for comparison. Through visual inspection and a quantitative index, the performance of the proposed algorithm in terms of the entire scene classification, various surface water types and noise suppression is comprehensively compared with those of the water index and support vector machine. Moreover, band optimization, image preprocessing and a training sample for the proposed algorithm are analyzed and discussed. We find that (1) based on the quantitative evaluation, the performance of the surface water extraction for the entire scene when using the MLP is better than that when using the water index or support vector machine. The overall accuracy of the MLP ranges from 98.25–100%, and the kappa coefficients of the MLP range from 0.965–1. (2) The MLP can precisely extract various surface water types and effectively suppress noise caused by shadows and ice/snow. (3) The 1–7-band composite provides a better band optimization strategy for the proposed algorithm, and image preprocessing and high-quality training samples can benefit from the accur...