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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

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