Open AccessShip Classification by the Fusion of Panchromatic Image and Multi-spectral Image Based on Pseudo Siamese LightweightNetwork
Author(s) -
Mengyang Li,
Weiwei Sun,
Xuan Du,
Xiaohan Zhang,
Libo Yao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012022
Subject(s) - panchromatic film , multispectral image , computer science , artificial intelligence , feature extraction , feature (linguistics) , image fusion , image (mathematics) , pattern recognition (psychology) , computer vision , convolutional neural network , feature detection (computer vision) , channel (broadcasting) , dimension (graph theory) , contextual image classification , fusion , remote sensing , image processing , geography , telecommunications , mathematics , philosophy , linguistics , pure mathematics
The current rapid development of the remote sensing satellite industry provides a large amount of image data for ship classification tasks. Aiming at the problem of insufficient feature extraction of single source image, this paper designs a lightweight ship classification model based on the fusion of panchromatic image and multispectral image of pseudo Siamese network to extract image features more fully. First, establish a multi-source remote sensing image ship target classification dataset MPFS (MS and PAN Ship image Fusion Classification Dataset); secondly, send panchromatic images and multispectral images to the network through different convolutional layers, thendesign a multi-level feature extraction network for panchromatic images and an adaptive feature extraction network for spectral imagesrespectively; finally, concatenate the features along the channel dimension and send them to the classification network.