Road and railway detection in SAR images using deep learning

Sen, Nigar
Olgun, Orhun
Ayhan, Oner
Detection and segmentation of motorways, railroads and other roads with similar features are significant for comprehension of both low and high resolution synthetic aperture radar (SAR) imagery. Separation of transportation network from other fields or features is important to understand area contained in SAR image (i.e. the road density can inform about characteristic of that area). Standard image processing methods are inadequate to detect multiple linear targets correctly where computer vision, especially deep learning, provides more insight about features for different type of roads which help better discrimination of multiple linear features like roads and railroads. State-of-art deep learning algorithms are proposed as solutions for understanding road characteristics and extraction of multiple roads. In this paper, a method which uses deep convolutional neural network (DeepLabv3+) backbone architecture is proposed to detect road and railways concurrently. Semantic segmentation of roads using SAR imagery is challenging since these images differ as ground sample distance changes with sensor types which creates a setback for establishing dataset for all sensors. Training set contains 3 classes (road, railway, other) with collected signatures from TerraSAR-X Spotlight images for classification. Proposed method shows robust performance when applied to other sensor and results are presented.


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Şahin, Halil İbrahim; Dural Ünver, Mevlüde Gülbin; Koç, Seyit Sencer; Department of Electrical and Electronics Engineering (2010)
In the scope of this thesis, simulation-based analyses and comparative evaluation of Synthetic Aperture Radar (SAR) image formation techniques, namely Time Domain Correlation, Range Stacking, Range Doppler and Chirp Scaling algorithms, are presented. For this purpose, first, the fundamental concepts of SAR such as SAR geometry, resolution and signal properties are explained. A broadside SAR simulator that provides artificial raw data as an input to the algorithms is designed and implemented. Then, the mathe...
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Özkaya, Meral; Temizel, Alptekin; Department of Information Systems (2009)
Roads are significant objects of an infrastructure and the extraction of roads from aerial and satellite images are important for different applications such as automated map generation and change detection. Roads are also important to detect other structures such as buildings and urban areas. In this thesis, the road extraction approach is based on Active Contour Models for 1- meter resolution gray level images. Active Contour Models contains Snake Approach. During applications, the road structure was sepa...
Road detection by mean shift segmentation and structural analysis
Dursun, Mustafa; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2012)
Road extraction from satellite or aerial images is a popular issue in remote sensing. Extracted road maps or networks can be used in various applications. Normally, maps for roads are available in geographic information systems (GIS), however these informations are not being produced automatically. Generally they are formed with the aid of human. Road extraction algorithms are trying to detect the roads from satellite or aerial images with the minimum in-teraction of human. Aim of this thesis is to analyze ...
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...
Road extraction from high resolution satellite images using adaptive boosting with multi-resolution analysis
Çınar, Umut; Çetin, Yasemin; Department of Information Systems (2012)
Road extraction from satellite or aerial imagery is a popular topic in remote sensing, and there are many road extraction algorithms suggested by various researches. However, the need of reliable remotely sensed road information still persists as there is no sufficiently robust road extraction algorithm yet. In this study, we explore the road extraction problem taking advantage of the multi-resolution analysis and adaptive boosting based classifiers. That is, we propose a new road extraction algorithm explo...
Citation Formats
N. Sen, O. Olgun, and O. Ayhan, “Road and railway detection in SAR images using deep learning,” 2019, vol. 11155, p. 0, Accessed: 00, 2020. [Online]. Available: