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

Abstract

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

CLC Number:

TP751.1

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

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