Premium
Improved region convolutional neural network for ship detection in multiresolution synthetic aperture radar images
Author(s) -
Xiao Qilin,
Cheng Yun,
Xiao Minlei,
Zhang Jun,
Shi Hongji,
Niu Lihui,
Ge Chenguang,
Lang Haitao
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5820
Subject(s) - synthetic aperture radar , computer science , convolutional neural network , artificial intelligence , convolution (computer science) , computer vision , deep learning , artificial neural network , inverse synthetic aperture radar , pattern recognition (psychology) , radar , radar imaging , remote sensing , geology , telecommunications
Summary Effectively obtaining the location and direction of the ship target is an important prerequisite for maritime traffic management and marine accident rescue. Thanks to the rapid development of the target detection methods based on deep learning, this article proposed a ship target detection method for multiresolution synthetic aperture radar (SAR) images based on improved region convolution neural network (R‐CNN). According to the characteristics of ship target in the SAR images, we make several improvements such as enlarging the input, proposal optimization, database target categorization, and weight balance on the basis of the standard Faster R‐CNN. The experimental results proved that the proposed method can detect target effectively and precisely in complicated scenes of multiresolution SAR images, such as in‐shore and dense targets. It has a good potential in practical application.