Water Quality Classification of Low-altitude Remote Sensing Image of Urban River Network Based on Fully Convolutional Neural Network

Abstract:

This paper presents a dense semantic labeling algorithm based on deep learning. Firstly, low-altitude remote sensing data are acquired and preprocessed,and a data set for deep learning is built. Secondly, a fully convolutional neural network is designed and trained on the data set. Finally, the trained neural network is used to predict water quality level for each pixel in the remote sensing images. The algorithm is verified on image data acquired by unmanned areial vehicle（UAV） through low-altitude remote sensing in Jiading District and Baoshan District, Shanghai. Average classification accuracy achieves 87.96% and 77.57%, respectively.

Key words:low-altitude remote sensing;water quality classification;fully convolutional neural network