Beril Sirmacek

Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory

Very high resolution satellite images provide valuable information to researchers. Among these, urban-area boundaries and building locations play crucial roles. For a human expert, manually extracting this valuable information is tedious. One possible solution to extract this information is using automated techniques. Unfortunately, the solution is not straightforward if standard image processing and pattern recognition techniques are used. Therefore, to detect the urban area and buildings in satellite images, we propose the use of scale invariant feature transform (SIFT) and graph theoretical tools. SIFT keypoints are powerful in detecting objects under various imaging conditions. However, SIFT is not sufficient for detecting urban areas and buildings alone. Therefore, we formalize the problem in terms of graph theory. In forming the graph, we represent each keypoint as a vertex of the graph. The unary and binary relationships between these vertices (such as spatial distance and intensity values) lead to the edges of the graph. Based on this formalism, we extract the urban area using a novel multiple subgraph matching method. Then, we extract separate buildings in the urban area using a novel graph cut method. We form a diverse and representative test set using panchromatic 1-m-resolution Ikonos imagery. By extensive testings, we report very promising results on automatically detecting urban areas and buildings.

A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images

Detecting buildings from very high resolution (VHR) aerial and satellite images is extremely useful in map making, urban planning, and land use analysis. Although it is possible to manually locate buildings from these VHR images, this operation may not be robust and fast. Therefore, automated systems to detect buildings from VHR aerial and satellite images are needed. Unfortunately, such systems must cope with major problems. First, buildings have diverse characteristics, and their appearance (illumination, viewing angle, etc.) is uncontrolled in these images. Second, buildings in urban areas are generally dense and complex. It is hard to detect separate buildings from them. To overcome these difficulties, we propose a novel building detection method using local feature vectors and a probabilistic framework. We first introduce four different local feature vector extraction methods. Extracted local feature vectors serve as observations of the probability density function (pdf) to be estimated. Using a variable-kernel density estimation method, we estimate the corresponding pdf. In other words, we represent building locations (to be detected) in the image as joint random variables and estimate their pdf. Using the modes of the estimated density, as well as other probabilistic properties, we detect building locations in the image. We also introduce data and decision fusion methods based on our probabilistic framework to detect building locations. We pick certain crops of VHR panchromatic aerial and Ikonos satellite images to test our method. We assume that these crops are detected using our previous urban region detection method. Our test images are acquired by two different sensors, and they have different spatial resolutions. Also, buildings in these images have diverse characteristics. Therefore, we can test our methods on a diverse data set. Extensive tests indicate that our method can be used to automatically detect buildings in a robust and fast manner in Ikonos satellite and our aerial images.

Road Network Detection Using Probabilistic and Graph Theoretical Methods

Road network detection from very high resolution satellite and aerial images has diverse and important usage areas such as map generation and updating. Although an expert can label road pixels in a given image, this operation is prone to errors and quite time consuming. Therefore, an automated system is needed to detect the road network in a given satellite or aerial image in a robust manner. In this paper, we propose such a novel system. Our system has three main modules: probabilistic road center detection, road shape extraction, and graph-theory-based road network formation. These modules may be used sequentially or interchangeably depending on the application at hand. To show the strengths and weaknesses of our system, we tested it on several very high resolution satellite (Geoeye, Ikonos, and QuickBird) and aerial image sets. We compared our system with the ones existing in the literature. We also tested the sensitivity of our system to different parameter values. Obtained results indicate that our system can be used in detecting the road network on such images in a reliable and fast manner.

Urban Area Detection Using Local Feature Points and Spatial Voting

Automatically detecting and monitoring urban regions is an important problem in remote sensing. Very high resolution aerial and satellite images provide valuable information to solve this problem. However, they are not sufficient alone for two main reasons. First, a human expert should analyze these very large images. There may be some errors in the operation. Second, the urban area is dynamic. Therefore, detection should be done periodically, and this is time consuming. To handle these shortcomings, an automated system is needed to detect the urban area from aerial and satellite images. In this letter, we propose such a method based on local feature point extraction using Gabor filters. We use these local feature points to vote for the candidate urban areas. Then, we detect the urban area using an optimal decision-making approach on the vote distribution. We test our method on a diverse panchromatic aerial and Ikonos satellite image set. Our test results indicate the possible use of our method in practical applications.

Building detection from aerial images using invariant color features and shadow information

Robust detection of buildings is an important part of the automated aerial image interpretation problem. Automatic detection of buildings enables creation of maps, detecting changes, and monitoring urbanization. Due to the complexity and uncontrolled appearance of the scene, an intelligent fusion of different methods gives better results. In this study, we present a novel approach for building detection using multiple cues. We benefit from segmentation of aerial images using invariant color features. Besides, we use the edge and shadow information for building detection. We also determine the shape of the building by a novel method.

Performance Evaluation for 3-D City Model Generation of Six Different DSMs From Air- and Spaceborne Sensors

Since remote sensing provides more and more sensors and techniques to accumulate data on urban regions, three-dimensional representations of these complex environments gained much interest for various applications. In order to obtain three-dimensional representations, one of the most practical ways is to generate Digital Surface Models (DSMs) using very high resolution remotely sensed images from two or more viewing directions, or by using LIDAR sensors. Due to occlusions, matching errors and interpolation techniques these DSMs do not exhibit completely steep walls, and in order to obtain real three-dimensional urban models including objects like buildings from these DSMs, advanced methods are needed. A novel approach based on building shape detection, height estimation, and rooftop reconstruction is proposed to achieve realistic three-dimensional building representations. Our automatic approach consists of three main modules as; detection of complex building shapes, understanding rooftop type, and three-dimensional building model reconstruction based on detected shape and rooftop type. Besides the development of the methodology, the goal is to investigate the applicability and accuracy which can be accomplished in this context for different stereo sensor data. We use DSMs of Munich city which are obtained from different satellite (Cartosat-1, Ikonos, WorldView-2) and airborne sensors (3K camera, HRSC, and LIDAR). The paper later focuses on a quantitative comparisons of the outputs from the different multi-view sensors for a better understanding of qualities, capabilities and possibilities for applications. Results look very promising even for the DSMs derived from satellite data.

Integrating pedestrian simulation, tracking and event detection for crowd analysis

In this paper, an overall framework for crowd analysis is presented. Detection and tracking of pedestrians as well as detection of dense crowds is performed on image sequences to improve simulation models of pedestrian flows. Additionally, graph-based event detection is performed by using Hidden Markov Models on pedestrian trajectories utilizing knowledge from simulations. Experimental results show the benefit of our integrated framework using simulation and real-world data for crowd analysis.

Road Network Extraction Using Edge Detection and Spatial Voting

Road network detection from very high resolution satellite images is important for two main reasons. First, the detection result can be used in automated map making. Second, the detected network can be used in trajectory planning for unmanned aerial vehicles. Although an expert can label road pixels in a given satellite image, this operation is prone to errors. Therefore, an automated system is needed to detect the road network in a given satellite image in a robust manner. In this study, we propose a novel approach to detect the road network from a given panchromatic Ikonos satellite image. Our method has five main steps. First, we apply a nonlinear bilateral filtering to smooth the given image. Then, we extract Canny edges and the gradient information as local features. Using these local features, we generate a spatial voting matrix. This voting matrix indicates the possible locations of the road network pixels. By processing this voting matrix in an iterative manner, we detect initial road pixels. Finally, we apply a tracking algorithm on the voting matrix to detect the missing road pixels. We tested our method on various satellite images and provided the extracted road networks in the experiments section.

Automatic crowd density and motion analysis in airborne image sequences based on a probabilistic framework

Real-time monitoring of crowded regions has crucial importance to avoid overload of people in certain areas. Understanding behavioral dynamics of large people groups can also help to estimate future status of underground passages, public areas, or streets. In order to bring an automated solution to the problem, we propose a novel approach using airborne image sequences. Our approach depends on extraction of local features from invariant color components of the images. Using extracted local features as observations, we form probability density functions (pdf) for each image of input sequence which holds information about density of people. We introduce four measures to extract information about pdf characteristics. A change within the four measures over the image sequence gives important information about status of the crowds. Besides, we also use obtained pdfs to estimate main crowd motion directions. To test our algorithm, we use a stadium entrance image data set, and two festival area data sets taken from an airborne camera system. In order to be later able to reach real-time performance the algorithms use parameters which can be extracted directly from the image data. Experimental results indicate possible usage of the developed algorithms in real-life events.

Fusing stereo and multispectral data from WorldView-2 for urban modeling

Using the capability of WorldView-2 to acquire very high resolution (VHR) stereo imagery together with as much as eight spectral channels allows the worldwide monitoring of any built up areas, like cities in evolving states. In this paper we show the benefit of generating a high resolution digital surface model (DSM) from multi-view stereo data (PAN) and fusing it with pan sharpened multi-spectral data to arrive at very detailed information in city areas. The fused data allow accurate object detection and extraction and by this also automated object oriented classification and future change detection applications. The methods proposed in this paper exploit the full range of capacities provided by WorldView-2, which are the high agility to acquire a minimum of two but also more in-orbit-images with small stereo angles, the very high ground sampling distance (GSD) of about 0.5 m and also the full usage of the standard four multispectral channels blue, green, red and near infrared together with the additional provided channels special to WorldView-2: coastal blue, yellow, red-edge and a second near infrared channel. From the very high resolution stereo panchromatic imagery a so called height map is derived using the semi global matching (SGM) method developed at DLR. This height map fits exactly on one of the original pan sharpened images. This in turn is used for an advanced rule based fuzzy spectral classification. Using these classification results the height map is corrected and finally a terrain model and an improved normalized digital elevation model (nDEM) generated. Fusing the nDEM with the classified multispectral imagery allows the extraction of urban objects like like buildings or trees. If such datasets from different times are generated the possibility of an expert object based change detection (in quasi 3D space) and automatic surveillance will become possible.