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Building detection using HOG descriptors

Ilsever, M. | Ünsalan, Cem

Conference Object | 2013 | RAST 2013 - Proceedings of 6th International Conference on Recent Advances in Space Technologies , pp.115 - 119

One of the most important applications of remote sensing is detecting and analyzing man-made structures in aerial and satellite images. Buildings deserve specific consideration among man-made structures. Manual detection is the one way of labeling the buildings. Recent satellite image resolutions allow researchers manual detection. However, this approach is cumbersome and prone to errors. Therefore, an automated method is needed. Unfortunately, the solution is not straightforward due to various factors. In this study, we propose an adaptation of an earlier successful method, Histogram of Oriented Gradients, to building detection. We . . . test our method on several Ikonos satellite images and provide the results. © 2013 IEEE Daha fazlası Daha az

Multiscale tree analysis from satellite images

Ozcan, A.H. | Sayar, Y. | Hisar, D. | Ünsalan, Cem

Conference Object | 2015 | RAST 2015 - Proceedings of 7th International Conference on Recent Advances in Space Technologies , pp.265 - 269

As satellite images cover wide areas and obtaining them has become easier, using these images in agriculture has become an important research area. Especially, satellite images can be used in seasonal crop estimation. In this study, we focused on crop estimation from trees. The boundary of a tree is proportional to its age which gives information on the approximate crop that can be obtained from it. Obtaining the number of trees in a region, with the size of each tree, gives the approximate amount of crop that can be harvested from that region. In this study, we propose a method based on multiple filtering, watershed segmentation, a . . .nd Otsu thresholding to detect trees and their boundaries. To test the proposed method, we picked three satellite images containing 6928 trees. These trees have diameters between 2 to 30 pixels. We compared the proposed method with two other methods in the literature. We summarized the obtained results in this study. © 2015 IEEE Daha fazlası Daha az

Crowd detection in airborne images using spatial point statistics

Özcan, A.H. | Ünsalan, Cem | Reinartz, P.

Conference Object | 2015 | 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings , pp.419 - 422

The crowd density in public places increases in social events. If an emergency occurs during such events, authorities should take urgent measures to prevent causalities. Therefore, crowd detection and analysis is a critical research area. Even though there are several studies on person detection from street or indoor cameras, these may not be directly used to detect or analyze the crowd formed from people. In this study, we approach the problem using aerial images. We propose two novel methods to detect the crowd using spatial statistics. The first novel method is based on the first-order statistics. It uses the nearest neighbor rel . . .ations for each person in the image. The second novel method is based on the second-order statistics. Here, the spatial position of persons are checked whether they are clustered or randomly distributed. We test these two methods on a sample test image and provide performance measures. © 2015 IEEE Daha fazlası Daha az

Priority encoding of image data in wireless multimedia sensor networks for border surveillance

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

A color invariant based binary coded structured light range scanner for shiny objects

Benveniste, R. | Ünsalan, Cem

Conference Object | 2010 | Proceedings - International Conference on Pattern Recognition , pp.798 - 801

Object range data provide valuable information in recognition and modeling applications. Therefore, it is extremely important to reliably extract the range data from a given object. There are various range scanners based on different principles. Among these, structured light based range scanners deserve spacial attention. In these systems, coded light stripes are projected onto the object. Using the bending of these light stripes on the object and the triangulation principle, range information can be obtained. Since this method is simple and fast, it is used in most industrial range scanners. Unfortunately, these range scanners can . . .not scan shiny objects reliably. The main reason is either highlights on the shiny object or the ambient light in the environment. These disturb the coding by illumination. As the code is changed, the range data extracted from it will also be disturbed. In this study, we propose a color invariant based binary coded structured light range scanner to solve this problem. The color invariant used can eliminate the effects of highlights on the object and the ambient light from the environment. This way, we can extract the range data of shiny objects in a robust manner. To test our method, we developed a prototype range scanner. We provide the obtained range data of various test objects with our range scanner. © 2010 IEEE Daha fazlası Daha az

Final comments

İlsever, M. | Ünsalan, Cem

Book Part | 2012 | SpringerBriefs in Computer Science ( 9781447142546 ) , pp.71 - 72

Two dimensional change detection methods are used extensively in image processing and remote sensing applications. In this study, we focused on these methods and their application to satellite images. We grouped change detection methods (based on the way they process data) under four categories as: pixel based, texture based, transformation based, and structural. © 2012, Cem Ünsalan.

Road network detection using probabilistic and graph theoretical methods

Ünsalan, Cem | Sirmacek, B.

Article | 2012 | IEEE Transactions on Geoscience and Remote Sensing50 ( 11 PART1 ) , pp.4441 - 4453

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 interchangeab . . .ly 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. © 1980-2012 IEEE Daha fazlası Daha az

Using empirical mode decomposition for ground filtering

Ozcan, A.H. | Ünsalan, Cem

Conference Object | 2015 | RAST 2015 - Proceedings of 7th International Conference on Recent Advances in Space Technologies , pp.317 - 321

LiDAR data provides valuable information for various remote sensing applications. For these, one important and challenging problem is ground filtering. This operation separates the bare earth and object data. Researchers proposed several methods to solve this problem. However, the complexity of the data limit the usability of these methods for all terrain types. Besides, the performance obtained in ground filtering should be improved further. In this study, we focus on this problem and propose a novel ground filtering method using Empirical Mode Decomposition (EMD). We tested the proposed method on the standard ISPRS data set and ev . . .aluate its strengths and weaknesses. We also compared the proposed method with the ones in the literature to show the improvements obtained. © 2015 IEEE Daha fazlası Daha az

Graph matching based change detection in satellite images

Ilsever, M. | Ünsalan, Cem

Conference Object | 2012 | International Geoscience and Remote Sensing Symposium (IGARSS) , pp.6213 - 6215

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, based on different principles, to detect changes from satellite images. In this study, we propose a novel change detection method based on structure information. Therefore, our method can be called as structural change detection. To summarize the structure in an image, we benefit from local features and their graph based representation. Extracting the structure from both images, we benefit f . . .rom graph matching to detect changes. We tested our method on 18 Ikonos image pairs and discuss its strengths and weaknesses. © 2012 IEEE Daha fazlası Daha az

Using local features to measure land development in urban regions

Sirmaçek, B. | Ünsalan, Cem

Article | 2010 | Pattern Recognition Letters31 ( 10 ) , pp.1155 - 1159

Monitoring urban development in a given region provides valuable information to researchers. Currently available, very high resolution satellite images can be used for this purpose. However, manually monitoring land development using these large and complex images is time consuming and prone to errors. To handle this problem, an automated system is needed to measure development in urban regions. Therefore, in this study we propose such an automated method to measure land development in a given urban region imaged in different times. We benefit from novel land development measures for this purpose. They are based on local features ob . . .tained from sequential images. As a novel contribution, we represent these local features in a spatial voting matrix. Then, we propose five different land development measures on the formed voting matrix. We test our method on 19 sets of sequential panchromatic Ikonos images. Our test results indicate the possible use of our method in measuring land development automatically. © 2009 Elsevier B.V. All rights reserved Daha fazlası Daha az

Building detection with spatial voting and morphology based segmentation

Özcan, A.H. | Ünsalan, Cem

Conference Object | 2016 | 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings , pp.429 - 432

Automated object detection in remotely sensed data has gained wide application areas due to increased sensor resolution. In this study, we propose a novel building detection method using high resolution DSM data and true orthophoto image. In the proposed method, DSM feature points and NDVI are obtained. Then, they are used for spatial voting to generate a building probability map. Local maxima of this map are used as seed points for segmentation. For this purpose, a morphology based segmentation method is proposed. This way, buildings are detected from DSM data. We tested our method on ISPRS semantic labeling dataset and obtained pr . . .omising results. © 2016 IEEE Daha fazlası Daha az


İlsever, M. | Ünsalan, Cem

Book Part | 2012 | SpringerBriefs in Computer Science ( 9781447142546 ) , pp.57 - 70

In this chapter, we provide experimental results obtained from all change detection methods considered in this study. We first explain the data set used in the experiments. Then, we provide performance results in tabular form for each change detection method in detail. © 2012, Cem Ünsalan.

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