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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.

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.

Automated digital terrain model (DTM) generation from remotely sensed data has gained wide application areas due to increased sensor resolution. In this study, a novel ground filtering and segmentation method is proposed for digital surface model (DSM) data. The proposed method starts with extracting DSM feature points. These are used in a probabilistic framework to generate a non-ground object probability map in spatial domain. Modes of this map are used as seed points in a novel segmentation method based on morphological operations. This leads to ground filtering and DTM generation. The method is tested on three different data sets. Two of these originate from light detection and ranging (lidar) sensors, where resulting kappa coefficient (?) range mostly higher than 95% for differently structured urban areas. Also, the visual appearance of the generated DTM exhibits obvious improvements over all other investigated methods. The third data set is a DSM obtained from WorldView-2 stereo image pairs. Also here, we compare our results with three different methods in the literature. Although the DSM quality is much lower, more than 85% of ? can be reached by the proposed method, showing its superiority over other methods. Overall experimental results show that the proposed method can be used reliably for DTM generation. The results also indicate that the method has prominent advantages in comparison to established methodologies in terms of robustness in handling urban areas of different properties. Moreover, there are only few parameters to adjust in the proposed method, and these are independent of the object size in DSM data. © 2018 Informa UK Limited, trading as Taylor & Francis Group. 1,14e+201 This work is supported by TUBITAK through project no 114E199.

Crop yield forecasting in a region has become an important research area due to global warming and related climate changes. Although this can be performed by available statistical information, obtaining recent and up to date data to extract reliable statistical information is not easy. Very high resolution satellite images can be used for this purpose. However, manually processing these images acquired from large regions is neither feasible nor reliable. Therefore, automated methods are needed for this purpose. In this study, we propose a novel method to help forecasting the crop yield in an orchard. The number of trees in an orchard with the size and type of each tree crown gives an approximate crop that can be harvested. Therefore, we focus on both tree crown detection and delineation for this purpose. The proposed method for tree crown detection is based on probabilistic voting. For tree crown delineation, we propose a watershed segmentation based ellipse fitting method. We tested the proposed method on 17 satellite images containing 13,476 trees. We compared the method with the classical local maxima/minima filtering and a recent method in literature using three more test images. These tests indicate the strengths and weaknesses of the proposed method. © 2017 Informa UK Limited. 1,14e+201 This work is supported by TUBITAK through project no 114E199.

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

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 promising results. © 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

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 evaluate its strengths and weaknesses. We also compared the proposed method with the ones in the literature to show the improvements obtained.

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

Recent sensors give valuable data for remote sensing applications. Among these, building and change detection are important problems. Therefore, researchers worked on these problems using both 2D and 3D data. Some previous studies used only 2D data due to their availability. Yet others used either 3D data alone or 2D and 3D data in a joint manner. Besides, some studies only focused on building detection. Yet others used detected building information in change detection. In this study, we focus on 3D change detection based on building information. Therefore, we first detect buildings. At this step, we benefit from both 2D and 3D data. Then, we locate changes based on these detected buildings. We detect building pixels using panchromatic, multispectral, and Digital Surface Model (DSM) data using a decision tree classifier. Then, we refine the detected building pixels using morphological and shape based operations. Finally, we apply an object based hierarchical change detection method on the refined pixels. We tested our method on 780 buildings and obtained promising results. © 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.