深度学习U-Net方法及其在高分辨卫星影像分类中的应用

高分辨率遥感影像有精确的几何结构和空间布局，但是光谱信息有限，增大了对光谱特征相似地物的分类难度。针对高分辨率遥感影像分类的问题，采用深度学习U-Net模型分类方法。基于黑河下游额济纳绿洲高分二号遥感影像，通过U-Net模型提取胡杨、柽柳、耕地、草地和裸地五种地物覆被类型，分类总体精度和Kappa系数分别为85.024%和0.795 6，并与传统的支持向量机（SVM, Support Vector Machine）和面向对象的分类方法比较，结果表明：相对于SVM和面向对象，基于U-Net模型的高分辨率卫星影像地物覆被分类，能够更好地对地物本质特征进行提取，分类效果较好，满足精度要求。

U-Net Neural Networks and Its Application in High Resolution Satellite Image Classification

High-resolution remote sensing images have precise geometric structure and spatial layout, but the spectral information is limited, which increases the difficulty of classifying similar features of spectral features. Aiming at the problem of high resolution remote sensing image classification, a U-Net convolutional neural network classification method based on deep learning is proposed. Based on the remote sensing image of the Ejina Oasis GF-2 in the lower reaches of the Heihe River, the U-Net model was used to extract the five types of land cover types of Populus euphratica, Tamarix chinensis, cultivated land, grassland and bare land. The overall classification accuracy and Kappa coefficient were 85.024% and 0.795 6 respectively. Compared with the traditional Support Vector Machine(SVM) and object-oriented method, the results show that compared with SVM and object-oriented method, the U-Net model is used to classify the high-resolution remote sensing, and the classification effect is better. The ground extracts the essential features of the features to meet the accuracy requirements.