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 corner 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.
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 (κ) 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 κ 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.
ARTICLE HISTORY
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.
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