Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138233
Title: Object detection from satellite imagery
Authors: Seah, Yi Xuan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2020
Publisher: Nanyang Technological University
Project: SCSE 19-0043
Abstract: Satellite imagery has been used to observe and collect information about the earth for decades. Objects such as vehicles, planes, and ships can be detected from these imageries. However, as the imageries often lack contrast details that are critical to the effectiveness of fast and accurate detection techniques. Thus, Machine Learning techniques such as Deep Learning are required to process the imageries in a fast and accurate manner. This report will be investigating Deep Learning-based object detection techniques. Image classification techniques such as VGG and ResNet will be studied to determine which is more suitable for satellite imagery. Object Detection techniques such as R-CNN, Fast R-CNN, and Faster R-CNN will be also be studied to understand the progress of object detection methods. Lastly, tests using metrics such as mean average precision (mAP) and inference time will be used to determine the suitability of object detection for satellite imagery.
URI: https://hdl.handle.net/10356/138233
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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