Signal and Information Processing

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

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  • School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, Hubei, China

Received date: 2021-09-13

  Online published: 2023-09-28

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.

Cite this article

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 . DOI: 10.3969/j.issn.0255-8297.2023.05.004

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