Application of a convolutional neural network to land use classification based on GF-2 remote sensing imagery

Traditional remote sensing-based land use classification methods mainly focus on pixel-based unsupervised and supervised approaches and object-based image analysis (OBIA). The primary objective of this study is to improve the land use classification accuracy by introducing a convolutional neural net...

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Veröffentlicht in:	Arabian journal of geosciences Jg. 14; H. 21
Hauptverfasser:	Yu, Jun, Du, Shizhou, Xin, Zhenghua, Huang, Linsheng, Zhao, Jinling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.11.2021 Springer Nature B.V
Schlagworte:	Ablation Accuracy Algorithms Artificial neural networks Classification Classification schemes Cultivated lands Earth and Environmental Science Earth science Earth Sciences Entropy Entropy (Information theory) Fallow land Image analysis Image classification Image processing Imagery Iterative methods Kernel functions Land use Land use classification Neural networks Objective function Optimization Original Paper Remote sensing Resolution Spatial discrimination Spatial resolution Spectral bands Support vector machines Training Support vector machine GF-2 Convolutional neural network Land use Remote sensing
ISSN:	1866-7511, 1866-7538
Online-Zugang:	Volltext
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