Imbalanced Remote Sensing Ship Image Classification

doi:10.23940/ijpe.19.06.p22.17091715

Abstract

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.

Key words: imbalanced ship classification, deep learning, decision tree

Sizhe Huang, Huosheng Xu, and Xuezhi Xia. Imbalanced Remote Sensing Ship Image Classification [J]. Int J Performability Eng, 2019, 15(6): 1709-1715.

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