Automated building detection from satellite images by using shadow information as an object invariant

Yüksel, Barış
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 generated from the shadow regions using the direction of illumination obtained from image metadata. For each landscape, foreground (building) and background pixels are automatically determined and a bipartitioning is obtained using a graph-based algorithm, Grabcut. Finally, local results are merged to obtain the final building detection result. Considering performance evaluation results, this approach can be seen as a proof of concept that the shadow is an invariant for a building object and promising detection results can be obtained when even a single invariant for an object is used.


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 building extraction from high resolution satellite images for map updating: A model based approach
San, D. Koc; TÜRKER, MUSTAFA (2007-10-12)
An approach was developed for automatically updating the buildings of an existing vector database from high resolution satellite images using spectral image classification, Digital Elevation Models (DEM) and the model-based extraction techniques. First, the areas that contain buildings are detected using spectral image classification and the normalized Digital Surface Model (nDSM). The classified output provides the shapes and the approximate locations of the buildings. However, those buildings that have si...
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...
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 ...
Automatic segmentation of VHR images using type information of local structures acquired by mathematical morphology
AYTEKİN, orsan; Ulusoy, İlkay (Elsevier BV, 2011-10-01)
The morphological profile (MP) and differential morphological profile (DMP) have been used extensively to acquire spatial information to be used in the segmentation of very high resolution (VHR) remotely sensed images. In most of the previous approaches, the maxima of the MP and DMP were investigated to estimate the best representative scale in the spatial domain for the pixel under consideration. Then, the object type (i.e. dark, bright or flat) was estimated based on the location of the maximum. Finally, ...
Citation Formats
B. Yüksel, “Automated building detection from satellite images by using shadow information as an object invariant,” M.S. - Master of Science, Middle East Technical University, 2012.