Inshore Ship Detection in Sar Images Based on Deep Neural Networks

2018 ◽  
Author(s):  
Lei Liu ◽  
Guowei Chen ◽  
Zongxu Pan ◽  
Bin Lei ◽  
Quanzhi An
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Sar Images ◽  
Ship Detection

scholarly journals Ship-Iceberg Classification in SAR and Multispectral Satellite Images with Neural Networks

2020 ◽  
Vol 12 (15) ◽  
pp. 2353
Author(s):  
Henning Heiselberg
Keyword(s):  
Neural Networks ◽  
Satellite Images ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
Neural Nets ◽  
The Arctic ◽  
Support Vector ◽  
Sar Images ◽  
Multispectral Satellite Images ◽  
The Cost

Classification of ships and icebergs in the Arctic in satellite images is an important problem. We study how to train deep neural networks for improving the discrimination of ships and icebergs in multispectral satellite images. We also analyze synthetic-aperture radar (SAR) images for comparison. The annotated datasets of ships and icebergs are collected from multispectral Sentinel-2 data and taken from the C-CORE dataset of Sentinel-1 SAR images. Convolutional Neural Networks with a range of hyperparameters are tested and optimized. Classification accuracies are considerably better for deep neural networks than for support vector machines. Deeper neural nets improve the accuracy per epoch but at the cost of longer processing time. Extending the datasets with semi-supervised data from Greenland improves the accuracy considerably whereas data augmentation by rotating and flipping the images has little effect. The resulting classification accuracies for ships and icebergs are 86% for the SAR data and 96% for the MSI data due to the better resolution and more multispectral bands. The size and quality of the datasets are essential for training the deep neural networks, and methods to improve them are discussed. The reduced false alarm rates and exploitation of multisensory data are important for Arctic search and rescue services.

scholarly journals TOWARDS DISTILLATION OF DEEP NEURAL NETWORKS FOR SATELLITE ON-BOARD IMAGE SEGMENTATION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1553-1559
Author(s):  
F. de Vieilleville ◽  
A. Lagrange ◽  
R. Ruiloba ◽  
S. May
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Data Analysis ◽  
Deep Neural Networks ◽  
Distillation Method ◽  
Ship Detection ◽  
Promising Solution

Abstract. Cubesats platforms expansion increases the need to simplify payloads and to optimize downlink data capabilities. A promising solution is to enhance on-board software, in order to take early decisions, automatically. However, the most efficient methods for data analysis are generally large deep neural networks (DNN) oversized to be loaded and processed on limited hardware capacities of cubesats. To use them, we must reduce the size of DNN while accommodating efficiency in terms of both accuracy and inference cost. In this paper, we propose a distillation method which reduces image segmentation deep neural network’s size to fit into on board processors. This method is presented through a ship detection example comparing accuracy and inference costs for several networks.

scholarly journals AUTOMATIC SHIP DETECTION IN SINGLE-POL SAR IMAGES USING TEXTURE FEATURES IN ARTIFICIAL NEURAL NETWORKS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 395-399 ◽  
Author(s):  
E. Khesali ◽  
H. Enayati ◽  
M. Modiri ◽  
M. Mohseni Aref
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Texture Features ◽  
Speckle Noise ◽  
Sar Images ◽  
Time Saving ◽  
Ship Detection ◽  
Operational Solution ◽  
Novel Method ◽  
Artificial Neural

This paper presents a novel method for detecting ships from high-resolution synthetic aperture radar (SAR) images. This method categorizes ship targets from single-pol SAR images using texture features in artificial neural networks. As such, the method tries to overcome the lack of an operational solution that is able to reliably detect ships with one SAR channel. The method has the following three main stages: 1) feature extraction; 2) feature selection; and 3) ship detection. The first part extracts different texture features from SAR image. These textures include occurrence and co occurrence measures with different window sizes. Then, best features are selected. Finally, the artificial neural network is used to extract ship pixels from sea ones. In post processing stage some morphological filters are used to improve the result. The effectiveness of the proposed method is verified using Sentinel-1 data in VV polarization. Experimental results indicate that the proposed algorithm can be implemented with time-saving, high precision ship extraction, feature analysis, and detection. The results also show that using texture features the algorithm properly discriminates speckle noise from ships.

Deep learning facilitated local deformation monitoring with large-scale SAR interferometry

2021 ◽  
Author(s):  
Teng Wang ◽  
Heng Luo ◽  
Zhipeng Wu ◽  
Lv Fu ◽  
Qi Zhang
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Local Deformation ◽  
Deformation Monitoring ◽  
Sar Interferometry ◽  
Localized Deformation ◽  
Sar Images ◽  
Image Stack

<p>SAR interferometry has stepped in the big-data era, particularly with the acquisition capability and open-data policy of ESA’s Sentinel-1 SAR mission. Large amount of Sentinel-1 SAR images has been acquired and archived, allowing for generating thousands of interferograms, covering millions of square kilometers. In such a large-scale interferometry scenario, many applications still focus on monitoring kilometer-scale local deformation, sparsely distributed in a large area. It is thus not effective to apply the time-series InSAR analysis to the whole image stack, but to focus on areas with deformation. Aiming at this target, we present our recent work built upon deep neural networks to firstly detect localized deformation and then carry on the time-series analysis on small interferogram patches with deformation signals.</p><p>Here, we first introduce our burst-based Sentinel-1 processor, which has been fully paralleled for large-scale InSAR processing. From these interferograms, we adapt and train several deep neural networks for masking decorrelation areas, detecting local deformation, and unwrapping high-gradient phases. We apply our networks for mining subsidence and landslides monitoring. Comparing with traditional time-series InSAR analysis, the presented strategy not only reduces the computation time, but also avoids the influence of large-scale tropospheric delays and the propagation of possible unwrapping errors.</p><p>The presented methods introduce artificial intelligence to the time-series InSAR processing chain and make the mission of regularly monitoring localized deformation sparsely distributed in large scale feasible and more efficient. As future work, we can further improve the temporal resolution of InSAR based local deformation monitoring by training networks combining interferograms from C-band and L-band SAR images, which will be available soon from future SAR missions such as NiSAR and LuTan-1.</p>

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