A Shadow based trainable method for building detection in satellite images

Dikmen, Mehmet
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 illumination direction and spectral properties of segments are used to classify them as belonging to a building or not. Then, a resegmentation is performed to extract building patches by merging only the neighboring segments, which are classified as building. Next, a postprocessing step is implemented to eliminate some false building patches. Finally, a one class modeling approach was introduced to refine extracted building patches. The approach was tested on several Google Earth images of varying characteristics in order to examine the effects of the change in illumination direction, shadow amount and building variety (size, shape, density, etc.). The results were examined by both pixel and object based performance evaluation methods. Best results were obtained on images having relatively shorter shadows and captured almost at the nadir. Best quality for the extracted patches and the least false detections were also observed in the same case.


A Fast shape detection approach by directional integrations
Okman, Osman Erman; Akar, Gözde; Department of Electrical and Electronics Engineering (2013)
Detection and identification of objects from aerial images are important problems for various types of application areas. For many of the man-made structures shape is a fundamental feature by which these objects are separated from the background and other structures. In this thesis, a novel geometric shape detection algorithm based on the spatial properties of structures is proposed. Since the objects are transformed into 1-D vectors by evaluating directional integrals and detections occur by the analysis o...
A Feature Extraction Method for Marble Tile Classification
DEVİREN, Murat; M KORAY, Balcı; Leloğlu, Uğur Murat; SEVERCAN, Mete (2000-03-03)
This study focuses on a feature extraction algorithm for classification of marble tiles. The color content and vein distribution are considered to be the main criteria for classification. A color segmentation algorithm is used for detection of veins. The shape analysis of the veins are done by utilizing the distance image.
Automated building detection from satellite images by using shadow information as an object invariant
Yüksel, Barış; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2012)
Apart from classical pattern recognition techniques applied for automated building detection in satellite images, a robust building detection methodology is proposed, where self-supervision data can be automatically extracted from the image by using shadow and its direction as an invariant for building object. In this methodology; first the vegetation, water and shadow regions are detected from a given satellite image and local directional fuzzy landscapes representing the existence of building are generate...
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...
An automatic geo-spatial object recognition algorithm for high resolution satellite images
Ergul, Mustafa; Alatan, Abdullah Aydın (2013-09-26)
This paper proposes a novel automatic geo-spatial object recognition algorithm for high resolution satellite imaging. The proposed algorithm consists of two main steps; a hypothesis generation step with a local feature-based algorithm and a verification step with a shape-based approach. In the hypothesis generation step, a set of hypothesis for possible object locations is generated, aiming lower missed detections and higher false-positives by using a Bag of Visual Words type approach. In the verification s...
Citation Formats
M. Dikmen, “A Shadow based trainable method for building detection in satellite images,” Ph.D. - Doctoral Program, Middle East Technical University, 2014.