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Remote sensing mainly focuses on information extraction from data acquired by sensors on satellite and aerial platforms. Here, one such area of interest is ground object detection and shape extraction. Recently launched satellites and conventional aerial platforms (such as commercial UAV and professional drones) have sensors leading to more detailed and rich data source for this purpose. From these, data most of the times come in the form of optical images and LiDAR measurements. Resolution of this acquired data has increased significantly such that most ground objects (as buildings, trees, ships, cars, airplanes) can be detected and analyzed in detail. Therefore, computer vision methods have become extremely useful in remote sensing applications such as building detection and shape extraction for urban planning; tree crown measurement for crop yield forecasting; ship detection for monitoring unlawful fishery; car detection for traffic flow monitoring and intelligent transportation; and airplane detection for military and commercial operations. Researchers proposed several methods to automate the mentioned applications since manually handling them is extremely hard and prohibitively time consuming. Unfortunately, the proposed methods focus on one object type most of the times. Therefore, there is no general method to handle all the mentioned applications using computer vision tools. To overcome this problem, we propose a general framework for object detection and shape extraction in remote sensing data. Our method is based on probabilistic representation inspired by our previous work and perceptual organization principles. Due to space limitations, we only focus on buildings, trees, ships, airplanes, and cars as objects of interest in this study. We test the proposed method on several optical images acquired by different satellites and LiDAR data obtained from an aerial platform. For all objects of interest, we provide test results on both object detection and shape extraction steps. We analyze the proposed method based on these tests and discuss its strengths and weaknesses. We also comment on possible future extensions of the proposed method.

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. 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 voting based method on grayscale satellite images. To test the proposed method, we picked eight satellite images containing 2668 trees. We summarized the obtained results in this study. © 2016 IEEE. 24th Signal Processing and Communication Application Conference, SIU 2016 -- 16 May 2016 through 19 May 2016 -- -- 122605

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, and 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. Aselsan;e al.;HAVELSAN;Roketsan;TAI;TURKSAT 7th International Conference on Recent Advances in Space Technologies, RAST 2015 -- 16 June 2015 through 19 June 2015 -- -- 116912

Automatic extraction of bare-Earth LiDAR points to generate Digital Terrain Model (DTM) is still an ongoing problem. Even though there are several methods for ground filtering, automatic and adaptive methods are still a need due to the complexity of the environment. In this study, we address the ground filtering problem by applying Empirical Mode Decomposition (EMD) to the airborne LiDAR data. EMD is a data-driven method that adapts to the local characteristics of the signal. We benefit from EMD to extract the local trend of the LiDAR height data. This way, can extract a local adaptive threshold to filter ground and non-ground objects. We tested our method using the ISPRS LiDAR reference dataset and obtained promising results. We also compared the filtering results with the ones in the literature to show the improvements obtained. © 2015 IEEE. 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- -- 113052

Crowd monitoring is an important task of security forces. If an emergency occurs during large events, authorities should take urgent measures to prevent causalities. Also understanding crowd dynamics such as tracking crowds or sparse people goups before an emergency occurs is a need. Therefore, crowd detection and analysis is a critical research area. There are several studies for crowd monitoring that use street or indoor cameras which may not be directly used for analyzing large crowds. In this study, we approach the problem using aerial images. We propose two novel methods. In the first method, we use first-order spatial point statistics. It uses the nearest neighbor relations for each person in the image to detect crowd regions. Our second method also uses the first order statistics with an additional sparse people group detection flexibility. We test the proposed methods on two aerial images and provide quantitative test results. © 2015 IEEE. Aselsan;e al.;HAVELSAN;Roketsan;TAI;TURKSAT 7th International Conference on Recent Advances in Space Technologies, RAST 2015 -- 16 June 2015 through 19 June 2015 -- -- 116912

Ship detection in satellite images is used for monitoring illegal fishing and violation of coastal waters or general maritime management. SAR images has been extensively used in this manner. Recently, researchers have started using optical satellite images for ship detection. These studies can be divided into two categories as inshore and offshore ship detection. Offshore ship detection is a relatively easy problem. But, inshore ship detection is a challenging problem in which ships are located closely. In this study, we handle this problem and propose a novel method assuming we have the harbor information beforehand. In the method, the mask is morphologically thickened in single steps and adjacent ships are detected with a contour following method. Moreover, a shape model for the ships is used to eliminate false alarms. We tested the proposed method on high resolution satellite images and achieved promising results. © 2015 IEEE. 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- -- 113052

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 relations 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. 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- -- 113052

Among different remote sensing applications, change detection deserves specific consideration. The importance of this area is its applicability on damage assessment after natural disasters. Fortunately, recent sensors allow researchers to develop advanced change detection methods. Some of these benefit from panchromatic or multispectral remote sensing images, whereas others use 3D data besides the 2D information. In this study, we benefit from both 2D and 3D data to detect changes in buildings. We specifically focused on building change detection, since after a natural disaster damaged building information is one of the most important one. Our building change detection method is based on our previous study based on probabilistic building detection. In this study, we first extract corner points using the Harris corner detector from panchromatic images. These corner points are used on Digital Surface Model (DSM) data to estimate possible building locations. To do so, we represent possible building locations via a kernel based density estimation method. In this study, we use the difference of the bitemporal estimated kernel maps (obtained in two different times) for change detection. Then, we apply a morphology based shape refinement method. As a result, we can detect changes in the scene. We tested our method on WorldView-2 sensor images with 780 buildings. The results are promising. © 2014 IEEE. 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 -- 23 April 2014 through 25 April 2014 -- Trabzon -- 106053

In this paper, sea clutter radar plots are modeled by spatial point processes. A test procedure is proposed to analyze "Complete Spatial Randomness (CSR)" characteristics of radar plot locations. Plot intensity map is also constructed. This map is separated into two sub-regions; cutter region and moving target region. This map can be used as a reliability metric for target detection algorithms.

The oscillating and swinging parts of a target observed by radar cause additional frequency modulation and induce sidebands in the target's Doppler frequency shift (micro-Doppler). This effect provides unique features for classification in radar systems. In this paper, the micro-Doppler spectra and range-Doppler matrices of single bird and bird flocks are obtained by simulations for linear FMCW radar. Obtained range-Doppler matrices are compared for single bird and bird flock under several scenarios and new features are proposed for classification.