Road extraction from high resolution satellite images using adaptive boosting with multi-resolution analysis

Download

index.pdf

Date

2012

Author

Çınar, Umut

Metadata

Show full item record

Item Usage Stats

178
views

93
downloads

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 exploiting both spectral and structural features of the high resolution multi-spectral satellite images. The proposed model is composed of three major components; feature extraction, classification and road detection. Well-known spectral band ratios are utilized to represent reflectance properties of the data whereas a segmentation operation followed by an elongatedness scoring technique renders structural evaluation of the road parts within the multi-resolution analysis framework. The extracted features are fed into Adaptive Boosting (Adaboost) learning procedure, and the learning method iteratively combines decision trees to acquire a classifier with a high accuracy. The road network is identified from the probability map constructed by the classifier suggested by Adaboost. The algorithm is designed to be modular in the sense of its extensibility, that is; new road descriptor features can be easily integrated into the existing model. The empirical evaluation of the proposed algorithm suggests that the algorithm is capable of extracting majority of the road network, and it poses promising performance results.

Subject Keywords

Remote-sensing images., High resolution imaging., Image processing, Image analysis.

URI

http://etd.lib.metu.edu.tr/upload/12614650/index.pdf
https://hdl.handle.net/11511/21741

Collections

Graduate School of Informatics, Thesis

Suggestions

OpenMETU
Core

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 ...
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...
Automatic multi-scale-segmentation of high spatial resolution satellite images using watersheds Şahin, Kerem; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2013) Useful information extraction from satellite images for the use of other higher level applications such as road network extraction and update, city planning etc. is a very important and active research area. It is seen that pixel-based techniques becomes insufficient for this task with increasing spatial resolution of satellite imaging sensors day by day. Therefore, the use of object-based techniques becomes indispensable and the segmentation method selection is very crucial for object-based techniques. In ...
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...
Road extraction from satellite images by self-supervised classification and perceptual grouping Sahin, E.; Ulusoy, İlkay (2013-01-01) A fully automatized method which can extract road networks by using the spectral and structural features of the roads is proposed. First, Anti-parallel Centerline Extraction (ACE) is used to obtain road seed points. Then, the road seeds are improved with perceptual grouping method and the road class is determined with Maximum Likelihood Estimation (MLE) by modeling the seed points with Gaussian Mixture. The morphological operations (opening, closing and thinning) are performed for improving classification r...

Citation Formats

U. Çınar, “Road extraction from high resolution satellite images using adaptive boosting with multi-resolution analysis,” M.S. - Master of Science, Middle East Technical University, 2012.