الفهرس | Only 14 pages are availabe for public view |
Abstract Deep learning (DL), an innovative version of neural network, becomes a hotspot research topic in the Remote Sensing (RS) field. Recently, different deep learning architectures have been developed to meet the challenges of spatial big data era. In this thesis, a comprehensive investigation of deep learning approaches was introduced to better handle spatial big data. Following is an outline of the three primary contributions made in this thesis: • Object Detection is an important and difficult challenge in remote sensing imagery for civilian applications such as traffic monitoring, military applications, and ship/Airplane detection. These applications are critical for decision-makers at this time. The adaptive mask Region Based Convolutional Neural Networks (RCNN) was utilized to detect multi-scale objects in optical remote sensing images. Experiments were conducted to determine the efficacy of the following optimization methods: Adam, SGD, RMSprop, Adadelta, hybrid SGD_Adam, and hybrid Adam_SGD in RS domain. According to the obtained results, the average precision (AP) of the different optimization methods were 90.8%, 87.7%, 87.3%, 48.6% and 91.2%, respectively. Using SWATS (switch from Adam to SGD) in the phase of training. SWATS reduced computation cost and time while achieving excellent accuracy. The proposed adaptive MaskR-CNN outperformed other deep learning object detection methods in terms of mean AP such as FRCN, YOLO, YOLO2, SSD, R_FCN were 95%, 76.4%, 66.7%, 79.7, 89.4%, 92.8%, respectively. |