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Imbalanced Remote Sensing Ship Image Classification

Volume 15, Number 6, June 2019, pp. 1709-1715
DOI: 10.23940/ijpe.19.06.p22.17091715

Sizhe Huanga,b, Huosheng Xub, and Xuezhi Xiab

aCollege of Information Technology, Harbin Engineering University, Harbin, 150000, China
bWuhan Digital Engineering Research Institute, Wuhan, 430000, China

 

(Submitted on December 15, 2018; Revised on January 16, 2019; Accepted on February 11, 2019)

Abstract:

Aiming at the unbalanced classification problem of remote sensing ship image datasets in ship target classification and the problem that the traditional decision tree classification algorithm needs to rely on artificial construction features to realize classification, a weighted deep neural decision forest is proposed. This method combines deep learning with resampling. The results show that the method can achieve a better classification accuracy than the traditional decision tree on unbalanced classification of ship target.

 

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