Road and railway detection in SAR images using deep learning

Nigar Şen; Orhun Olgun; Öner Ayhan

doi:10.1117/12.2532803

7 October 2019 Road and railway detection in SAR images using deep learning

Nigar Şen, Orhun Olgun, Öner Ayhan

Proceedings Volume 11155, Image and Signal Processing for Remote Sensing XXV; 111550D (2019) https://doi.org/10.1117/12.2532803
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

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.

Conference Presentation

Citation Download Citation

Nigar Şen, Orhun Olgun, and Öner Ayhan "Road and railway detection in SAR images using deep learning", Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111550D (7 October 2019); https://doi.org/10.1117/12.2532803

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