A Neural Network Based on Consistency Learning and Adversarial Learning for Semi-supervised Synthetic Aperture Radar Ship Detection

2022 ◽  
pp. 1-1
Author(s):  
Biao Hou ◽  
Zitong Wu ◽  
Bo Ren ◽  
Zhonghua Li ◽  
Xianpeng Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Adversarial Learning ◽  
Ship Detection ◽  
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.

Improved region convolutional neural network for ship detection in multiresolution synthetic aperture radar images

2020 ◽  
Vol 32 (22) ◽  
Author(s):  
Qilin Xiao ◽  
Yun Cheng ◽  
Minlei Xiao ◽  
Jun Zhang ◽  
Hongji Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Convolutional Neural Network ◽  
Synthetic Aperture ◽  
Ship Detection ◽  
Radar Images ◽  
Aperture Radar

Balance learning for ship detection from synthetic aperture radar remote sensing imagery

2021 ◽  
Vol 182 ◽  
pp. 190-207
Author(s):  
Tianwen Zhang ◽  
Xiaoling Zhang ◽  
Chang Liu ◽  
Jun Shi ◽  
Shunjun Wei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Remote Sensing ◽  
Remote Sensing Imagery ◽  
Ship Detection ◽  
Aperture Radar

Validation of Ship Detection by the RADARSAT Synthetic Aperture Radar and the Ocean Monitoring Workstation

2000 ◽  
Vol 26 (3) ◽  
pp. 200-212 ◽  
Author(s):  
P.W. Vachon ◽  
S.J. Thomas ◽  
J. Cranton ◽  
H.R. Edel ◽  
M.D. Henschel
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Ship Detection ◽  
Ocean Monitoring ◽  
Aperture Radar

scholarly journals Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qingyan Meng ◽  
Linlin Zhang ◽  
Qiuxia Xie ◽  
Shun Yao ◽  
Xu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Landsat 8 ◽  
Combined Use ◽  
Artificial Neural ◽  
Agricultural Areas ◽  
Aperture Radar

Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

Deep convolutional neural network for P-band spaceborne synthetic aperture radar imaging through the ionosphere

2020 ◽  
Vol 14 (04) ◽  
Author(s):  
Hongyin Shi ◽  
Jing Zhang ◽  
Er-Fang Gao ◽  
Ting Yang ◽  
Jianwen Guo
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Convolutional Neural Network ◽  
Radar Imaging ◽  
Deep Convolutional Neural Network ◽  
Synthetic Aperture ◽  
Aperture Radar

An automatic ship detection scheme in open ocean for Synthetic Aperture Radar images

2009 ◽  
Author(s):  
Weibin Li ◽  
Bormin Huang ◽  
Mingyi He
Keyword(s):  
Synthetic Aperture Radar ◽  
Open Ocean ◽  
Synthetic Aperture ◽  
Detection Scheme ◽  
Ship Detection ◽  
Radar Images ◽  
Aperture Radar
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