Ship detection in synthetic aperture radar (SAR) images by deep learning

Date

2019-01-01

Author

Ayhan, Oner
Sen, Nigar

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

394
views

0
downloads

In this paper, we propose a Convolutional Neural Network (CNN) based method to detect ships in Synthetic Aperture Radar (SAR) images. The architecture of proposed CNN has customized parts to detect small targets. In order to train, validate and test the CNN, TerraSAR-X Spot mode images are used. In the phase of data preparation, a GIS (Geographic Information System) specialist labels ships manually in all images. Later, image patches that contain ships are cropped and ground truths are also obtained from pre-labeled data. In the stage of train, data augmentation is used and the data divided into three parts: (i) train, (ii) validation, (iii) test. The training takes almost a day of duration with a NVIDIA GTX 1080 Ti graphic card. Results on test data shows that our method has promising detection performance for the ship targets on both open water and near harbors.

Subject Keywords

Ship detection, CNNs, SAR

URI

https://hdl.handle.net/11511/66161

DOI

https://doi.org/10.1117/12.2532781

Collections

Unclassified, Conference / Seminar

Suggestions

OpenMETU
Core

Region Based Target Detection Approach for Synthetic Aperture Radar Images and Its Parallel Implementation Nar, Fatih; Demirkesen, Can; Okman, O. Erman; ÇETİN, MÜJDAT (2012-04-26) Automatic target detection (ATD) methods for synthetic aperture radar (SAR) imagery are sensitive to image resolution, target size, clutter complexity, and speckle noise level. However, a robust ATD method needs to be less sensitive to the above factors. In this study, a constant false alarm rate (CFAR) based method is proposed which can perform target detection independent of image resolution and target size even in heterogeneous background clutter. The proposed method is computationally efficient since cl...
Sea Detection on High-Resolution Panchromatic Satellite Images Using Texture and Intensity Besbinar, Beril; Alatan, Abdullah Aydın (2014-01-01) In this paper, a two-stage sea-land mask detection algorithm on high resolution panchromatic images is proposed. An initial mask is generated using texture features in the first stage and this mask is refined by using intensity values in the second stage. Image is divided into windows and the Local Binary Patterns (LBP) histograms, evaluated at each window, are modelled using the sea and land sample spaces obtained by the altitude information which has very low resolution compared to the image. These models...
Airport runway detection in satellite images by Adaboost Learning ZÖNGÜR, Ugur; Halıcı, Uğur; AYTEKİN, Orsan; Ulusoy, İlkay (2009-09-03) Advances in hardware and pattern recognition techniques, along with the widespread utilization of remote sensing satellites, have urged the development of automatic target detection systems in satellite images. Automatic detection of airports is particularly essential, due to the strategic importance of these targets. In this paper, a runway detection method using a segmentation process based on textural properties is proposed for the detection of airport runways, which is the most distinguishing element of...
Land-cover Classification in SAR Images using Dictionary Learning Aktas, Gizem; Bak, Cagdas; Nar, Fatih; Sen, Nigar (2015-09-24) Land-cover classification in Synthetic Aperture Radar (SAR) images has significance in both civil and military remote sensing applications. Accurate classification is a challenging problem due to variety of natural and man-made objects, seasonal changes at acquisition time, and diversity of image reconstruction algorithms.. In this study, Feature Preserving Despeckling (FPD), which is an edge preserving total variation based speckle reduction method, is applied as a preprocessing step. To handle the mention...
Deep convolutional neural networks for airport detection in remote sensing images Budak, Umit; Sengur, Abdulkadir; Halıcı, Uğur (2018-05-05) This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not o...

Citation Formats

O. Ayhan and N. Sen, “Ship detection in synthetic aperture radar (SAR) images by deep learning,” 2019, vol. 11169, p. 0, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66161.