FSINet: A Robust Feature Separation and Integration Network for Multiscale SAR Object Detection

Deep learning has rapidly progressed in synthetic aperture radar (SAR) image object detection. However, complex noise, intricate backgrounds, and multiscale objects impact the performance of SAR image object detection. To solve these problems, in this article, we propose a feature separation and int...

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Detalhes bibliográficos

Principais autores:	An Wei, Song Liu, Congxuan Zhang, Shuaiqi Liu, Kaicheng Xu, Zhen Chen
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2026-01-01
Colecção:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Assuntos:	Convolutional neural network (CNN) feature fusion object detection synthetic aperture radar (SAR)
Acesso em linha:	https://ieeexplore.ieee.org/document/11397651/
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