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In this study, the issue of intent classification in frequently asked questions for chatbots is discussed. There are many studies on this subject. However, most of the studies were conducted for the English language, and the issue of improving the intent classification results for small data sets has not been adequately examined. For this purpose, frequently asked questions data were used with subword semantic hashing method and its effects in Turkish and English languages were examined. According to the results, we observe an increase in classification performance of the subword semantic hashing method in Turkish language and the improvement is significantly higher compared to the English language. In addition, the classification performance results of pretrained the subword semantic hashing is better than the results of neural network models such as CLIP and FastText.

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

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

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.

The oscillating and swinging parts of a target observed by radar cause additional frequency modulation and induce sidebands in the target's Doppler frequency shift (micro-Doppler). This effect provides unique features for classification in radar systems. In this paper, the micro-Doppler spectra and range-Doppler matrices of single bird and bird flocks are obtained by simulations for linear FMCW radar. Obtained range-Doppler matrices are compared for single bird and bird flock under several scenarios and new features are proposed for classification.

In this paper, statistical modeling of clutter data measured by a noncoherent S-band marine radar mounted on a fixed position is presented. Characterization is done by finding the best fitted density function to the clutter over eight candidate distribution. Real-time parameter estimation of the predetermined distribution and automatic threshold detection for Constant False Alarm Rate (CFAR) is provided.