Ship Matching Using Convolutional Neural Network in Multi-source Synthetic Aperture Radar Images

2020 ◽  
Vol 102 (sp1) ◽  
Author(s):  
Lihui Niu ◽  
Haitao Lang
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Convolutional Neural Network ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Aperture Radar

Noise Reduction in Synthetic Aperture Radar Images Using Convolutional Neural Network

2018 ◽  
Vol 15 (8) ◽  
pp. 2572-2576
Author(s):  
Kishore Medhi ◽  
Amitab Khwairakpam ◽  
V. Dhilip Kumar ◽  
Debdatta Kandar
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Convolutional Neural Network ◽  
Noise Reduction ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Aperture Radar

scholarly journals Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images

2019 ◽  
Vol 11 (23) ◽  
pp. 2862 ◽  
Author(s):  
Weiwei Fan ◽  
Feng Zhou ◽  
Xueru Bai ◽  
Mingliang Tao ◽  
Tian Tian
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
False Alarm ◽  
Convolutional Neural Network ◽  
False Alarm Rate ◽  
Synthetic Aperture ◽  
Deep Convolutional Neural Networks ◽  
Ship Detection ◽  
Sensing Applications ◽  
Aperture Radar

Ship detection plays an important role in many remote sensing applications. However, the performance of the PolSAR ship detection may be degraded by the complicated scattering mechanism, multi-scale size of targets, and random speckle noise, etc. In this paper, we propose a ship detection method for PolSAR images based on modified faster region-based convolutional neural network (Faster R-CNN). The main improvements include proposal generation by adopting multi-level features produced by the convolution layers, which fits ships with different sizes, and the addition of a Deep Convolutional Neural Network (DCNN)-based classifier for training sample generation and coast mitigation. The proposed method has been validated by four measured datasets of NASA/JPL airborne synthetic aperture radar (AIRSAR) and uninhabited aerial vehicle synthetic aperture radar (UAVSAR). Performance comparison with the modified constant false alarm rate (CFAR) detector and the Faster R-CNN has demonstrated that the proposed method can improve the detection probability while reducing the false alarm rate and missed detections.

A neural network for enhancing boundaries and surfaces in synthetic aperture radar images

1999 ◽  
Vol 12 (3) ◽  
pp. 499-511 ◽  
Author(s):  
Ennio Mingolla ◽  
William Ross ◽  
Stephen Grossberg
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Aperture Radar
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