基于多特征卷积神经网络的哨兵二号影像地物分类

doi:10.3969/j.issn.0255-8297.2023.05.004

摘要/Abstract

摘要： 空间分辨率为10 m的哨兵二号影像在原始的GoogLeNet中以影像的光谱值作为输入，没有将影像中的地物视为一个整体对象，为了利用影像的面向对象特征，提出了基于多特征的Object-oriented GoogLeNet网络结构。Object-oriented GoogLeNet在原有模型的基础上，引入了面向对象的光谱特征和形状特征，充分利用了不同地物间差异的形状特征进行分类。在武汉市及其周边的无云影像制作的数据集上，Object-oriented GoogLeNet模型的分类结果总体精度在GoogLeNet基础上提升了1.773%。结果表明，引入面向对象的特征模型在哨兵二号遥感影像分类中效果更好。

关键词: 哨兵二号, GoogLeNet, 深度学习, 形状特征, 土地利用分类

Abstract: The 10 m resolution Sentinel-2 images takes the spectral values of the image as input in the original GoogLeNet without treating the ground objects in the image as a whole. To leverage object-oriented features in Sentinel-2 remote sensing image classification, this paper proposes an Object-oriented GoogLeNet network structure based on multiple features. Object-oriented GoogLeNet incorporates object-oriented spectral and shape features, and fully utilizes the shape features of differences between different ground objects for classification. On the data set of cloudless images in Wuhan and its surrounding areas, the overall accuracy of the classification results of Object-oriented GoogLeNet model has increased by 1.773% compared to GoogLeNet. The results show that the model with object-oriented features enhances the classification performance of Sentinel-2 remote sensing images.

Key words: Sentinel-2, GoogLeNet, deep learning, shape features, land use classification

中图分类号:

TP751.1

黄显培, 孟庆祥. 基于多特征卷积神经网络的哨兵二号影像地物分类[J]. 应用科学学报, 2023, 41(5): 766-776.

HUANG Xianpei, MENG Qingxiang. Land Cover Classification of Sentinel-2 Image Based on Multi-feature Convolution Neural Network[J]. Journal of Applied Sciences, 2023, 41(5): 766-776.

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基于多特征卷积神经网络的哨兵二号影像地物分类

Land Cover Classification of Sentinel-2 Image Based on Multi-feature Convolution Neural Network

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