Remote sensing image classification method based on active-learning deep neural network

Wen Xiao; Junqiu Zou; Kuan Xu

doi:10.1117/12.2504455

6 November 2018 Remote sensing image classification method based on active-learning deep neural network

Wen Xiao, Junqiu Zou, Kuan Xu

Proceedings Volume 10817, Optoelectronic Imaging and Multimedia Technology V; 108171G (2018) https://doi.org/10.1117/12.2504455
Event: SPIE/COS Photonics Asia, 2018, Beijing, China

Abstract

The deep neural network algorithm has been widely used in remote sensing image classification. However, training classifiers require a large number of marked samples, which are costly. We propose a method based on active learning deep neural network. Firstly, deep neural network algorithm uses training samples to obtain the initial classifier, and then active learning is used to choose the most informative samples from unmarked samples to be marked by experts, the marked samples will be rejoined into training samples, in this way to update the classifier iteratively. This method requires only a small amount of training samples to achieve or even exceed the classification accuracy that a large number of training samples can achieve.

Citation Download Citation

Wen Xiao, Junqiu Zou, and Kuan Xu "Remote sensing image classification method based on active-learning deep neural network", Proc. SPIE 10817, Optoelectronic Imaging and Multimedia Technology V, 108171G (6 November 2018); https://doi.org/10.1117/12.2504455

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