Ozcan, A.H. | Ünsalan, Cem
Article | 2017 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10 ( 1 ) , pp.360 - 371
LiDAR technology is advancing. As a result, researchers can benefit from high-resolution height data from Earth's surface. Digital terrain model (DTM) generation and point classification (filtering) are two important problems for LiDAR data. These are connected problems since solving one helps solving the other. Manual classification of LiDAR point data could be time consuming and prone to errors. Hence, it would not be feasible. Therefore, researchers proposed several methods to solve DTM generation and point classification problems. Although these methods work fairly well in most cases, they may not be effective for all scenarios. . . . To contribute in this research topic, a novel method based on two-dimensional (2-D) empirical mode decomposition (EMD) is proposed in this study. Local, nonlinear, and nonstationary characteristics of EMD allow better DTM generation. The proposed method is tested on two publicly available LiDAR dataset, and promising results are obtained. Besides, the proposed method is compared with other methods in the literature. Comparison results indicate that the proposed method has certain advantages in terms of performance. © 2008-2012 IEEE Daha fazlası Daha az
Article | 2006 | IEEE Geoscience and Remote Sensing Letters3 ( 4 ) , pp.546 - 550
Land use classification is one of the major problems in remote sensing. Previous studies focused on multispectral information, texture-based features, and features based on edge detection to classify land usage from satellite images. In a previous study, structural features are introduced to classify land development using high-resolution satellite images. These structural features were based on line support regions (LSRs). LSRs are introduced to detect and represent straight lines in images using a pixel-grouping process. The structural features are calculated on these grouped pixels. It is shown that gradient-magnitude-based pixel . . . grouping may also be used in structural feature calculations. Therefore, the aim of this letter is twofold. First, the previous structural feature calculation method is shown to be more general than the LSR. Second, LSR-based features are shown to require fairly high computation compared to gradient-magnitude-based features with similar classification performance. © 2006 IEEE Daha fazlası Daha az
Ilsever, M. | Altunkaya, U. | Ünsalan, Cem
Conference Object | 2012 | International Geoscience and Remote Sensing Symposium (IGARSS) , pp.6185 - 6187
Change detection from bitemporal satellite images (taken from the same region in different times) may be used in various applications such as forest monitoring, earthquake damage assessment, and unlawful occupation. There are various approaches to detect changes from satellite images. One set of methods utilizes pixel based operations. In this study, we propose an alternative method for pixel based change detection using an ensemble of fuzzy and binary logic operations. We tested our method on 18 Ikonos image pairs and discuss its strengths and weaknesses compared to the existing methods in the literature. © 2012 IEEE.
Irgan, K. | Ünsalan, Cem | Baydere, Ş.
Conference Object | 2010 | Lecture Notes in Electrical Engineering62 LNEE , pp.191 - 194
25th International Symposium on Computer and Information Sciences, ISCIS 2010 -- 22 September 2010 through 24 September 2010 -- London -- 82255
Conference Object | 2007 | Proceedings of the 3rd International Conference on Recent Advances in Space Technologies, RAST 2007 , pp.345 - 348
Change detection using satellite images is of major concern to government agencies, military, and scientists. Government agencies can monitor the construction activity on a region automatically by detecting changes. They can update maps accordingly. Forests in satellite images may be monitored automatically for any change in their condition. As for military applications, strategic plans may be updated based on the observed changes on targets. After a natural disaster, even if that region can not be reached, automatically detecting changes in the region's satellite image may provide necessary information to measure the effect of the . . .damage. To solve all these and related problems, there exists commercially available high resolution satellite images. The problem here is automatically detecting changes using these images. In this study, we introduce a method using multispectral information to detect changes on Ikonos imagery. © 2007 IEEE Daha fazlası Daha az
Sirmaçek, B. | Ünsalan, Cem
Conference Object | 2009 | 2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009 , pp.812 - 815
Monitoring urbanization is an important problem in remote sensing. Very high resolution satellite images provide valuable information to solve this problem. However, these images are not sufficient alone for two main reasons. First, a human expert should analyze very large images. There may always be some errors in operation. Second, urban regions are dynamic. Therefore, monitoring urbanization should be done periodically. This is time consuming. To handle these shortcomings, an automated system is needed to monitor urbanization. In this study, we propose an automated method to detect urban areas in very high resolution satellite im . . .ages. Our work can be taken as the first and most important step for automatically monitoring urbanization. Our method is based on local feature extraction using Gabor filters. These local features are represented in a voting space. Using it the urban regions are detected automatically. We test our method on a diverse panchromatic Ikonos image set. Our test results indicate the possible use of our method in practical applications. © 2009 IEEE Daha fazlası Daha az
Sirmacek, B. | Ünsalan, Cem
Conference Object | 2011 | RAST 2011 - Proceedings of 5th International Conference on Recent Advances in Space Technologies , pp.188 - 192
Detecting the urban area from very high resolution satellite images provides very useful results for urban planning and land use analysis. Since manual detection is very time consuming and prone to errors, automated systems to detect the urban area from very high resolution satellite images are needed. Unfortunately, diverse characteristics of the urban area and uncontrolled appearance of remote sensing images (illumination, viewing angle, etc.) increase the difficulty to develop automated systems. In order to overcome these difficulties, in this study we propose a novel urban area detection method using local features and a probabi . . .listic framework. First, we introduce four different local feature extraction methods. Extracted local feature vectors serve as observations of the probability density function to be estimated. Using a variable kernel density estimation method, we estimate the corresponding probability density function. Using modes of the estimated density, as well as other probabilistic properties, we detect urban area boundaries in the image. We also introduce data and decision fusion methods to fuse information coming from different feature extraction methods. Extensive tests on very high resolution panchromatic Ikonos satellite images indicate the practical usefulness of the proposed method. © 2011 IEEE Daha fazlası Daha az
Conference Object | 2007 | IEEE Transactions on Geoscience and Remote Sensing45 ( 12 ) , pp.3989 - 3999
Inferring land use from satellite images is extensively studied by the remote sensing and pattern recognition communities. In previous studies, the focus was on classifying large regions due to the resolution of available satellite images. Nowadays, very high-resolution satellite imagery (Ikonos and Quickbird) allows researchers to focus on more complex land-use problems such as monitoring development in urban regions. Solutions to these complex problems may improve the life standards of city residents. To this end, we focus on automatically monitoring construction zones using their very high-resolution panchromatic satellite images . . . through time. To monitor land development, we obtain sequential images of a selected region. Then, we extract features from each image in the sequence. Comparing values of these features, we expect to measure the degree of land development through time. In a similar study, we introduced graph theoretical measures over Ikonos imagery to measure organization in a given satellite image. This paper is an extension of our previous work with more powerful new features. Here, we first introduce a novel method to extract straight line segments using a least squares ellipse fitting. Then, we introduce four new graph theoretical features. More importantly, we introduce a novel method to embed the spatial information in gray-level co-occurrence matrix statistical features to measure land development. Finally, we test all our existing and new features to measure land development in 19 different urban construction zones. Our test set consists of Ikonos satellite images of these regions captured in separate times. © 2007 IEEE Conditional gray-level co-occurrence matrix (GLCM) features, fusion of features, graph theory, land development, line-segment extraction Daha fazlası Daha az
Sirmaçek, B. | Ünsalan, Cem
Conference Object | 2009 | RAST 2009 - Proceedings of 4th International Conference on Recent Advances Space Technologies , pp.249 - 252
Automatic detection of damaged buildings from aerial and satellite images is an important problem for rescue planners and military personnel. In this study, we present a novel approach for automatic detection of damaged buildings in color aerial images. Our method is based on color invariants for building rooftop segmentation. Then, we benefit from grayscale histogram to extract shadow segments. After building verification using shadow information, we define a new damage measure for each building. Experimentally, we show that using our damage measure it is possible to discriminate nearby damaged and undamaged buildings. We present o . . .ur experimental results on aerial images. ©2009 IEEE Daha fazlası Daha az
Ünsalan, Cem | Boyer, K.L.
Conference Object | 2004 | Proceedings - International Conference on Pattern Recognition2 , pp.64 - 67
Cities are evolving and districts are changing their characteristics faster than ever before. Although the evolution is slow in the central parts of most cities, it is typically fairly fast in outlying regions. They affect the public and private utility networks and maps become less reliable. As a result, emergency plans based on these maps may be ineffective. To assist experts, planners, policy makers, and civil defense organizations, we are developing automated techniques. In previous work, we considered discriminating rural and urban regions . To automate the fine classification process, this paper introduces graph theoretical . . . measures over grayscale images. These measures are monotonic with increasing structure (organization) in the image. Thus, increased cultural activity and land development are indicated by increases in these measures - without explicit extraction of road networks, buildings, residences etc. We present a theoretical basis for the measures followed by extensive experimental results. We consider commercial IKONOS data, which are metric images. Our dataset is large and diverse, including sea and coastline, rural, forest, residential, industrial, and urban areas. On this data set we obtained promising results Daha fazlası Daha az
Ozcan, A.H. | Ünsalan, Cem | Reinartz, P.
Conference Object | 2013 | RAST 2013 - Proceedings of 6th International Conference on Recent Advances in Space Technologies , pp.139 - 143
Detecting and locating buildings in satellite images has various application areas. Unfortunately, manually detecting buildings is hard and very time consuming. Therefore, in the literature several methods are proposed to automatically detect buildings. These methods can be divided into two main groups. In the first group, researchers used panchromatic or multispectral information to detect buildings. In the second group, researchers used DSM data to detect buildings. In this study, we propose two novel methods to detect buildings by combining the panchromatic and DSM data. The first method uses corner points extracted by Harris cor . . .ner detection method. These corner points are used jointly with DSM data. Using a kernel based density estimation method, possible building locations are detected. In the second method, shadow of buildings are used in a similar way. We tested both methods on WorldView-2 images and DSM data generated from them. © 2013 IEEE Daha fazlası Daha az
Sirmacek, B. | Ünsalan, Cem
Article | 2009 | IEEE Transactions on Geoscience and Remote Sensing47 ( 4 ) , pp.1156 - 1167
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 d . . .etecting 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. © 2006 IEEE Daha fazlası Daha az