Open AccessSAR image classification based on multi‐feature fusion decision convolutional neural network
Author(s) -
Guo Liang
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12323
Subject(s) - softmax function , convolutional neural network , artificial intelligence , computer science , pattern recognition (psychology) , pooling , feature (linguistics) , feature extraction , margin (machine learning) , deep learning , contextual image classification , image (mathematics) , machine learning , linguistics , philosophy
With the success of convolutional neural network (CNN) in the field of image processing and image recognition, an improved fully convolutional neural network (FCNN) based on CNN is proposed. Although FCNN has better performance than CNN, the number of parameters of FCNN has also increased significantly compared to CNN. Consequently, this paper proposes a multi‐feature fusion decision convolutional neural network (MFFD‐CNN), which use two kinds down‐sample methods in the pooling layer, and chooses the concatenate or add operation as the way that consolidates the feature of the convolutional layer with a stride of 2 and the max‐pooling layer to execute fusion operations. Meanwhile replaces the traditional softmax loss with an additive margin Softmax (AM‐Softmax) loss. The structure, which is based on the multi‐target classification experiment results of the MSTAR data set, shows can not only obtain an average correct recognition rate 1.2% higher than FCNN, but also better stability without augmenting the training samples of MSTAR data.