Global appearance based airplane detection from satellite imagery

Arslan, Duygu
There is a rising interest in geospatial object detection due to not only the complexity of manual processing of such huge amount of data provided by high resolution satellite imagery but also for military application needs. A fundamental and yet state-of-the art approach for object detection is based on methods that utilize the global appearance. In such a holistic approach, the information of the object class is aimed to be modeled as a whole in the learning phase. And during the classification, a decision is taken at each window of the test image. In this thesis, two different discriminative methods are investigated for airplane detection from satellite images. In the first method, Haar-like features are used as weak classifiers for the airplane class representation. Then the AdaBoost learning algorithm is used to select the critical visual features that represent the airplanes best. Finally, a cascade of classifiers is constructed in order to speed-up the classifier. In the second method, a computationally efficient appearance-based algorithm for airplane detection is presented. An operator exploiting the edge information via gray level differences between the target and its background is constructed with Haar-like polygon regions using the shape information of the airplane as an invariant. The airplanes matching the operator are supposed to yield higher responses around the centroid of the object. Fast evaluation of the operator is achieved by means of integral image. The proposed algorithm has promising results in terms of accuracy in detecting aircraft type geospatial objects from satellite imagery.


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...
A Fully automatic shape based geo-spatial object recognition
Ergül, Mustafa; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2012)
A great number of methods based on local features or global appearances have been proposed in the literature for geospatial object detection and recognition from satellite images. However, since these approaches do not have enough discriminative capabilities between object and non-object classes, they produce results with innumerable false positives during their detection process. Moreover, due to the sliding window mechanisms, these algorithms cannot yield exact location information for the detected object...
Analysis of near-field ultra-wideband radar imaging algorithms
Arabacı, Ahmet; Koç, Seyit Sencer; 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...
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...
Road extraction from satellite images by self supervised classification and perceptual grouping
Şahin, Eda; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2013)
Road network extraction from high resolution satellite imagery is the most frequently utilized technique for updating and correcting geographic information system (GIS) databases, registering multi-temporal images for change detection and automatically aligning spatial datasets. This advance method is widely employed due to the improvements in satellite technology such as development of new sensors for high resolution imagery. To avoid the cost of the human interaction, various automatic and semi-automatic ...
Citation Formats
D. Arslan, “Global appearance based airplane detection from satellite imagery,” M.S. - Master of Science, Middle East Technical University, 2012.