Road network extraction from high-resolution multi spectral satellite images

Download

index.pdf

Date

2012

Author

Karaman, Ersin

Metadata

Show full item record

Item Usage Stats

214
views

121
downloads

In this thesis, an automatic road extraction algorithm for multi-spectral images is developed. The developed model extracts elongated structures from images by using edge detection, segmentation and clustering techniques. The study also extracts non-road regions like vegetative fields, bare soils and water bodies to obtain more accurate road map. The model is constructed in a modular approach that aims to extract roads with different characteristics. Each module output is combined to create a road score map. The developed algorithm is tested on 8-band WorldView-2 satellite images. It is observed that, the proposed road extraction algorithm yields 47 % precision and 70 % recall. The approach is also tested on the lower spectral resolution images with four-band, RGB and gray level. It is observed that the additional four bands provide an improvement of 12 % for precision and 3 % for recall. Road type analysis is also in the scope of this study. Roads are classified into asphalt, concrete and unpaved using Gaussian Mixture Models. Other linear objects such as railroads and water canals may also be extracted by this process. An algorithm that classifies drive roads and railroads for very high resolution images is also investigated. It is based on the Fourier descriptors that identify the presence of railroad sleepers. Water canals are also extracted in multi-spectral images by using spectral ratios that employ the near infrared bands. Structural properties are used to distinguish water canals from other water bodies in the image.

Subject Keywords

Remote-sensing images., Imaging systems., Image analysis., Image processing.

URI

http://etd.lib.metu.edu.tr/upload/12615362/index.pdf
https://hdl.handle.net/11511/22254

Collections

Graduate School of Informatics, Thesis

Suggestions

OpenMETU
Core

Image fusion for improving spatial resolution of multispectral satellite images Ünlüsoy, Deniz; Süzen, Mehmet Lütfi; Department of Geological Engineering (2013) In this study, four different image fusion techniques have been applied to high spectral and low spatial resolution satellite images with high spatial and low spectral resolution images to obtain fused images with increased spatial resolution, while preserving spectral information as much as possible. These techniques are intensity-hue-saturation (IHS) transform, principle component analysis (PCA), Brovey transform (BT), and Wavelet transform (WT) image fusion. Images used in the study belong to Çankırı reg...
Alignment of uncalibrated images for multi-view classification Arık, Sercan Ömer; Vural, Elif; Frossard, Pascal (2011-12-29) Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pairwise similarity measure between images, where a common choice is the Euclidean distance. However, the accuracy of the Euclidean distance as a similarity measure is restricted to cases where images are captured from nearby viewpoints. In settings with large transformations and viewpoint changes, alignment of im...
PROGRESSIVE COMPRESSION OF DIGITAL ELEVATION DATA USING MESHES Kose, Kivanc; Yılmaz, Erdal; ÇETİN, AHMET ENİS (2009-07-17) In this paper a new Digital Elevation Map (DEM) image compression algorithm is proposed. DEM image can be threated as a grayscale image, whose pixel values are the elevation values of the map points. The grayscale DEM image is compressed using an adaptive wavelet based image compression algorithm. The method, which is an extension of the progressive mesh compression takes advantage of the multiresolution property of the wavelets while coding the map images. This makes it possible to decode different resolut...
Superpixel based image sequence representation and motion estimation İnce, Kutalmış Gökalp; Alatan, Abdullah Aydın; Demirekler, Mübeccel; Department of Electrical and Electronics Engineering (2017) In this study a superpixel based representation of image sequences is proposed. For superpixel extraction, a novel gradient ascent approach, in which spatial and spectral statistics are utilized to obtain an optimal Bayesian classiﬁer for pixel to superpixel label assignment, is proposed. Utilization of the spectral and spatial statistics reduce the dependency on user selected global parameters, while increasing the robustness and adaptability. Proposed Local Adaptive Superpixels (LASP) approach exploits he...
A Shadow based trainable method for building detection in satellite images Dikmen, Mehmet; Halıcı, Uğur; Department of Geodetic and Geographical Information Technologies (2014) The purpose of this thesis is to develop a supervised building detection and extraction algorithm with a shadow based learning method for high-resolution satellite images. First, shadow segments are identified on an over-segmented image, and then neighboring shadow segments are merged by assuming that they are cast by a single building. Next, these shadow regions are used to detect the candidate regions where buildings most likely occur. Together with this information, distance to shadows towards illuminati...

Citation Formats

E. Karaman, “Road network extraction from high-resolution multi spectral satellite images,” Ph.D. - Doctoral Program, Middle East Technical University, 2012.