Akademik Veri Yönetim Sistemi
Neural Computing and Applications, cilt.36, sa.3, ss.1237-1259, 2024 (SCI-Expanded)
Satellite images, widely used in recent years and entered the field of remote sensing technology, continuously record image data in databases worldwide. These image data, which are continually obtained, changing, and have a large volume, fall into the big data category. On the other hand, CNN-based techniques, a sub-branch of artificial intelligence, have been widely used to classify and segment image data in recent years. The scope of this study, firstly, it was tried to determine spatial positions and building objects from large-volume satellite images with classical CNN methods. However, classical CNN models could not process the data even in ECW format, one of the most compressed satellite imagery forms. To overcome these problems, a new approach called DSHFS has been proposed. It uses hybrid CNN, GeoServer, and TileCache techniques related to different disciplines to detect structures from large-volume satellite images and their spatial locations. In the proposed DSHFS approach, large-volume satellite imagery is first published in WMS format with GeoServer, which has an open-source strategy. Then, the data are converted into small images containing coordinate information with the TileCache system in size 256 × 256 . Finally, the building objects in these images are detected by CNN models. In the proposed DSHFS approach, the actual locations of the detected structures on the earth are calculated using the location information of the image presented as input to the CNN model. In order to examine the performance of the proposed DSHFS approach, satellite imagery covering 12.5 GB in the computer system in ECW format, which corresponds to an area of approximately 17.200 km 2 of Kayseri province, was used. In the proposed DSHFS approach, Faster R-CNN, MobileNet, and YOLO models are used as the CNN model. When the proposed DSHFS approach is examined according to the F1 score, DSHFS Faster R-CNN, MobileNet, and YOLO obtain F1 scores of 0.961, 0.964, and 0.910, respectively. When evaluating the computational efficiency of the proposed approaches, it was found that DSHFS Faster R-CNN, MobileNet, and YOLO took 512.72, 188.20, and 99.35 s, respectively, to identify the structures and their locations in the image. DSHFS YOLO approach detected approximately 2 times faster than MobileNet and approximately 5 times faster than Faster R-CNN. When the proposed DSHFS approach is generally examined, it detects the building objects from the satellite image and their actual positions on the earth in approximately 0.13 s.