Automatic Extraction of the Non-Urban Roads from Satellite Images using Artificial Neural Network

Date

2020-10-25

Author

Ghaiziani, Matin
Konukseven, Erhan İlhan
Koku, Ahmet Buğra

Metadata

Show full item record

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

Item Usage Stats

90
views

9
downloads

Road detection from the satellite images can be considered as a classification process in which pixels are divided into the road and non-road classes. In this research, an automatic road extraction using an artificial neural network (ANN) based on automatic information extraction from satellite images and self-adjusting of the hidden layer proposed. Parameters of non-urban road networks from satellite images using a histogram-based binary image segmentation technique are also presented. The segmentation method is implemented by determining a global threshold, which is obtained from a statistical analysis of a number of sample satellite images and their ground truths. The thresholding method is based on two major facts: first, the points corresponding to non-asphalt roads are brighter than other areas in non-urban images. Second, it is observed that in an aerial image, the area covered by roads is only a small fraction of total pixels. It is also observed that pixels corresponding to roads are generally populated at the very bright end of the image greyscale histogram. In this method, at first, the possible road pixels are selected by the proposed segmentation method. Then different parameters, including color, gradient, and entropy, are computed for each pixel from the source image. Finally, these features are used for the artificial neural network input. The results show that the accuracy of the proposed road extraction method is around 80%.

URI

https://hdl.handle.net/11511/94176

DOI

https://doi.org/10.33793/acperpro.03.01.96

Conference Name

8th International Symposium on Innovative Technologies in Engineering and Science

Collections

Department of Mechanical Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Automatic building detection from high resolution satellite images Koc, D; Turker, M (2005-06-11) An approach was developed to update the buildings of existing vector database from high resolution satellite images using image classification, Digital Elevation Models (DEM) and object extraction techniques. First, the satellite image is classified using the Maximum Likelihood Classifier (MLC). The classified output provides the shapes and the approximate locations of the buildings. Next, a normalized Digital Surface Model (nDSM) is generated by subtracting the Digital Terrain Model (DTM) from the Digital ...
Geometric Camera Calibration of BiLSAT Small Satellite Preliminary Results Friedrich, Jurgen; Leloğlu, Uğur Murat; Tunalı, Erol (null; 2006-02-14) The interior geometric calibration of the multi-spectral camera of BiLSAT satellite is aimed using three sets of images from Ankara. Each set contains four different images for channels Red, Green, Blue (RGB), and Near Infra Red (NIR) in which well-defined target points were identified and their image coordinates measured. The target points’ UTM coordinates were extracted from aerial photos or topographic maps and collected from field using hand-held GPS receiver. A least square parameter estimation was the...
Automatic target recognition and detection in infrared imagery under cluttered background GÜNDOĞDU, ERHAN; KOÇ, AYKUT; Alatan, Abdullah Aydın (2017-09-14) Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object ...
Fuzzy spatial data cube construction and its use in association rule mining Işık, Narin; Yazıcı, Adnan; Department of Computer Engineering (2005) The popularity of spatial databases increases since the amount of the spatial data that need to be handled has increased by the use of digital maps, images from satellites, video cameras, medical equipment, sensor networks, etc. Spatial data are difficult to examine and extract interesting knowledge; hence, applications that assist decision-making about spatial data like weather forecasting, traffic supervision, mobile communication, etc. have been introduced. In this thesis, more natural and precise knowle...
Efficient algorithms for the frame packing and slot allocation of FlexRay V3.0 Çakmak, Cumhur; Schmidt, Şenan Ece; Schmidt, Klaus Verner; Department of Electrical and Electronics Engineering (2017) Recently, near-field ultra-wideband radar imaging algorithms have an important place in short range imaging applications by providing high resolution in both range and cross-range. In this study, the near-field ultra-wideband radar imaging algorithms in the literature such as Holographic Image Reconstruction Algorithm, Range Migration Algorithm, MIMO Based Range Migration Algorithm and MIMO Based Kirchhoff Migration Algorithm have been implemented using MATLAB. The algorithms are applied to modeled transmit...

Citation Formats

M. Ghaiziani, E. İ. Konukseven, and A. B. Koku, “Automatic Extraction of the Non-Urban Roads from Satellite Images using Artificial Neural Network,” presented at the 8th International Symposium on Innovative Technologies in Engineering and Science, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/94176.