Work place: Faculty of Engineering, Sana’a University, Sana’a, Yemen
E-mail: dalalm.ali@hotmail.com
Website:
Research Interests: Computational Learning Theory, Computer Architecture and Organization, Image Compression, Image Manipulation, Intrusion Detection System, Image Processing, Detection Theory
Biography
Dalal AL-Alimi has received her Master of Computer Science and Technology from School of Computer Science and Technology at China University of Geosciences (CUG), Wuhan, China. She received her B.Sc. Electrical Computer and IT from Engineering Faculty at Sana’a University, Sana’a, Yemen. Her research interests include Object Detection, Object Classification, Machine Learning, Deep Learning, AI, Image Processing and Analysis, Time Series Methods and IoT.
By Dalal AL-Alimi Yuxiang Shao Ahamed Alalimi Ahmed Abdu
DOI: https://doi.org/10.5815/ijitcs.2020.05.05, Pub. Date: 8 Oct. 2020
Geospatial imaging technique has opened a door for researchers to implement multiple beneficial applications in many fields, including military investigation, disaster relief, and urban traffic control. As the resolution of geospatial images has increased in recent years, the detection of geospatial objects has attracted a lot of researchers. Mask R-CNN had been designed to identify an object outlines at the pixel level (instance segmentation), and for object detection in natural images. This study describes the Mask R-CNN model and uses it to detect objects in geospatial images. This experiment was prepared an existing dataset to be suitable with object segmentation, and it shows that Mask R-CNN also has the ability to be used in geospatial object detection and it introduces good results to extract the ten classes dataset of Seg-VHR-10.
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