A coupled convolutional neural network for small and densely clustered ship detection in SAR images

2018 ◽  
Vol 62 (4) ◽  
Author(s):  
Juanping Zhao ◽  
Weiwei Guo ◽  
Zenghui Zhang ◽  
Wenxian Yu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sar Images ◽  
Ship Detection

scholarly journals High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1206 ◽  
Author(s):  
Tianwen Zhang ◽  
Xiaoling Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
High Speed ◽  
Earth Observation ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Images ◽  
Ship Detection ◽  
Detection Speed

As an active microwave sensor, synthetic aperture radar (SAR) has the characteristic of all-day and all-weather earth observation, which has become one of the most important means for high-resolution earth observation and global resource management. Ship detection in SAR images is also playing an increasingly important role in ocean observation and disaster relief. Nowadays, both traditional feature extraction methods and deep learning (DL) methods almost focus on improving ship detection accuracy, and the detection speed is neglected. However, the speed of SAR ship detection is extraordinarily significant, especially in real-time maritime rescue and emergency military decision-making. In order to solve this problem, this paper proposes a novel approach for high-speed ship detection in SAR images based on a grid convolutional neural network (G-CNN). This method improves the detection speed by meshing the input image, inspired by the basic thought of you only look once (YOLO), and using depthwise separable convolution. G-CNN is a brand new network structure proposed by us and it is mainly composed of a backbone convolutional neural network (B-CNN) and a detection convolutional neural network (D-CNN). First, SAR images to be detected are divided into grid cells and each grid cell is responsible for detection of specific ships. Then, the whole image is input into B-CNN to extract features. Finally, ship detection is completed in D-CNN under three scales. We experimented on an open SAR Ship Detection Dataset (SSDD) used by many other scholars and then validated the migration ability of G-CNN on two SAR images from RadarSat-1 and Gaofen-3. The experimental results show that the detection speed of our proposed method is faster than the existing other methods, such as faster-regions convolutional neural network (Faster R-CNN), single shot multi-box detector (SSD), and YOLO, under the same hardware environment with NVIDIA GTX1080 graphics processing unit (GPU) and the detection accuracy is kept within an acceptable range. Our proposed G-CNN ship detection system has great application values in real-time maritime disaster rescue and emergency military strategy formulation.

scholarly journals A Cascade Coupled Convolutional Neural Network Guided Visual Attention Method for Ship Detection From SAR Images

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 50693-50708 ◽  
Author(s):  
Juanping Zhao ◽  
Zenghui Zhang ◽  
Wenxian Yu ◽  
Trieu-Kien Truong
Keyword(s):  
Neural Network ◽  
Visual Attention ◽  
Convolutional Neural Network ◽  
Sar Images ◽  
Ship Detection

scholarly journals Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks

2020 ◽  
Vol 12 (1) ◽  
pp. 191 ◽  
Author(s):  
Jianhao Gao ◽  
Qiangqiang Yuan ◽  
Jie Li ◽  
Hai Zhang ◽  
Xin Su
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Image ◽  
Optical Data ◽  
Generative Adversarial Networks ◽  
Second Step ◽  
Missing Information ◽  
Sar Images ◽  
Generative Adversarial Network ◽  
Cloud Removal

The existence of clouds is one of the main factors that contributes to missing information in optical remote sensing images, restricting their further applications for Earth observation, so how to reconstruct the missing information caused by clouds is of great concern. Inspired by the image-to-image translation work based on convolutional neural network model and the heterogeneous information fusion thought, we propose a novel cloud removal method in this paper. The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function. Between the first step and the second step, the contrast and luminance of the simulated optical image are randomly altered to make the model more robust. Two simulation experiments and one real-data experiment are conducted to confirm the effectiveness of the proposed method on Sentinel 1/2, GF 2/3 and airborne SAR/optical data. The results demonstrate that the proposed method outperforms state-of-the-art algorithms that also employ SAR images as auxiliary data.

scholarly journals Oil Spill Detection in Quad-Polarimetric SAR Images Using an Advanced Convolutional Neural Network Based on SuperPixel Model

2020 ◽  
Vol 12 (6) ◽  
pp. 944 ◽  
Author(s):  
Jin Zhang ◽  
Hao Feng ◽  
Qingli Luo ◽  
Yu Li ◽  
Jujie Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Convolutional Neural Network ◽  
Oil Spill ◽  
Classification Accuracy ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Feature Sets ◽  
Oil Spill Detection ◽  
Component Decomposition

Oil spill detection plays an important role in marine environment protection. Quad-polarimetric Synthetic Aperture Radar (SAR) has been proved to have great potential for this task, and different SAR polarimetric features have the advantages to recognize oil spill areas from other look-alikes. In this paper we proposed an oil spill detection method based on convolutional neural network (CNN) and Simple Linear Iterative Clustering (SLIC) superpixel. Experiments were conducted on three Single Look Complex (SLC) quad-polarimetric SAR images obtained by Radarsat-2 and Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). Several groups of polarized parameters, including H/A/Alpha decomposition, Single-Bounce Eigenvalue Relative Difference (SERD), correlation coefficients, conformity coefficients, Freeman 3-component decomposition, Yamaguchi 4-component decomposition were extracted as feature sets. Among all considered polarimetric features, Yamaguchi parameters achieved the highest performance with total Mean Intersection over Union (MIoU) of 90.5%. It is proved that the SLIC superpixel method significantly improved the oil spill classification accuracy on all the polarimetric feature sets. The classification accuracy of all kinds of targets types were improved, and the largest increase on mean MIoU of all features sets was on emulsions by 21.9%.

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.

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