University of Twente Student Theses


Dense image matching using convolutional neural networks

Kimeu, J. Mamba (2020) Dense image matching using convolutional neural networks.

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Abstract:Demand for 3D geospatial products such as digital surface models (DSM) has increased rapidly over the past decades. They are finding applications in areas such as urban modelling, planning, construction and building, environmental mapping etc. Ground surveying, stereo-photogrammetry, and Airborne Laser Scanning (ALS) are some of the methods that have long been used to derive these products, but they are expensive and time-consuming. The emergence of Unmanned Aerial Vehicles (UAVs) platforms as a cost-effective mode of capturing aerial imagery has gained popularity in the field of geospatial engineering and remote sensing. Researchers and professionals are utilizing UAV systems to generate high-resolution 3D models for different applications. The automatic and fast generation of high-resolution 3D information presents an efficient and reliable alternative to the traditional (hand-crafted) methods. In this study, we developed a methodology to generate digital surface models using convolutional neural networks (CNNs). CNN's have been widely studied in computer vision for object recognition and segmentation tasks. They have also been applied in classification and stereo matching tasks and have shown to outperform hand-crafted methods due to their capability to learn high-level features. In this work, we designed a CNN architecture, optimized its parameters, and trained it end-to-end for disparity estimation using UAV imagery. We then recovered the 3D scenes from the disparity images and generated digital surface models. We compared our products with the state-of-the-art Pix4D software that relies on hand-crafted features. The results show that deep learning approaches are extremely fast than hand-crafted methods. On the quality of the products, our experimental results show our method generates a high-density point cloud that enables full recovery of scenes. Our DSM has smooth and sharp edges that resolve details in objects and structures. On comparison, we conclude that deep learning methods are quite fast and perform almost at par with conventional methods and have potential.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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