Ozcan, AH | Unsalan, C

Conference Object | 2015 | 2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) , pp.1224 - 1227

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 objec . . .ts. 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 Daha fazlası Daha az

Ozcan, AH | Unsalan, C

Conference Object | 2016 | 2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU) , 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 Daha fazlası Daha az

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

Özcan, A.H. | Ünsalan, Cem

Conference Object | 2015 | 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings , pp.1224 - 1227

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 objec . . .ts. 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 Daha fazlası Daha az

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