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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.

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.

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

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