Semi-automated extraction of surface water based on ZhuHai-1 hyperspectral satellite images

Abstract

ABSTRACT Surface water, as the most important land resource, profoundly affects the balance of the ecosystem and the development of social economy. The rapid development of remote-sensing technology provides the possibility of dynamic monitoring and real-time automatic extraction of surface water. Based on the index of ZhuHai-1 Orbita-hyperspectral (OHS) pixels, this paper proposes a supervised learning method for semi-automatic surface water extraction. The method first uses a threshold method to segment the cosine distance between pixel spectra, realizes automatic acquisition of training set labels required for supervised classification and effectively improves work efficiency. We then combine the extraction results of the support vector machine (SVM), which only considers spectral information, and the variant full convolutional neural network (VFCN), which only considers spatial information, to obtain more accurate surface water information. In this paper, the analysis of typical waterbodies in four regions showed that the performance of the surface water extraction for all scenes when using this method in this paper is better than that when using the water index (WI), VFCN or SVM. The overall accuracy of this method is above 98.822%, and the kappa coefficients are above 0.9019.