Traditional sand analysis is labor and cost-intensive, entailing specialized equipment and operators trained in geological analysis. Even a small step to automate part of the traditional geological methods could substantially improve the speed of such research while removing chances of human error. Digital image analysis techniques and computer vision have been well developed and applied in various fields but rarely explored for sand analysis. This research explores capabilities of remote sensing digital image analysis techniques, such as object-based image analysis (OBIA), machine learning, digital image analysis, and photogrammetry to automate or semi-automate the traditional sand analysis procedure. Here presented is a framework combining OBIA and machine learning classification of microscope imagery for use with unconsolidated terrigenous beach sand samples. Five machine learning classifiers (RF, DT, SVM, k-NN, and ANN) are used to model mineral composition from images of ten terrigenous beach sand samples. Digital image analysis and photogrammetric techniques are applied and evaluated for use to characterize sand grain size and grain circularity (given as a digital proxy for traditional grain sphericity). A new segmentation process is also introduced, where pixel-level SLICO superpixel segmentation is followed by spectral difference segmentation and further levels of superpixel segmentation at the object-level. Previous methods of multi-resolution and superpixel segmentation at the object level do not provide the level of detail necessary to yield optimal sand grain-sized segments. In this proposed framework, the DT and RF classifiers provide the best estimations of mineral content of all classifiers tested compared to traditional compositional analysis. Average grain size approximated from photogrammetric procedures is comparable to traditional sieving methods, having an RMSE below 0.05%. The framework proposed here reduces the number of trained personnel needed to perform sand-related research. It requires minimal sand sample preparation and minimizes user-error that is typically introduced during traditional sand analysis. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Resource managers frequently require moderate to high resolution imagery within short turnaround periods for use in a GIS-based management system. These spatial data can greatly enhance their ability to make timely, cost-saving decisions and recommendations. MBB Consulting Engineers, Inc., of Pietermaritzburg, South Africa had for many years made use of airborne videography to provide the imagery for several resource-based applications. Applications included detailed land use mapping in various South African river catchments and identification, density classification and mapping of alien vegetation. While the system was low cost and easy to operate, MBB had found that their system was inherently limited, particularly by its lack of automation and poor spatial resolution. This project was started because of a need to address these limitations and provide an airborne remote sensing system that was more automated and could produce higher resolution imagery than the existing system. In addition, the overall cost and time required to produce a map of the resource of interest needed to be reduced. The system developed in this project aimed to improve upon the pre-flight planning and in-flight image acquisition aspects of the existing system. No new post-flight image processing procedures were developed, but possible future refinement of the post-flight image processing routine was considered throughout the development of the system. A pre-flight planning software package was developed that could quickly and efficiently calculate the positions offlight lines and photographs or images with a minimum of user input. The in-flight image acquisition setup developed involved the integration of a high resolution digital still camera, a Global Positioning System (GPS), and camera control software. The use of the rapidly developing and improving technology of a digital still camera was considered to be a better alternative than a video graphic or traditional film camera system for a number of reasons. In particular, digital still cameras produce digital imagery without the need for development and scanning of aerial photographs or frame grabbing of video images. Furthermore, the resolution of current digital still cameras is already significantly better than that of video cameras and is rivalling the resolution of 35rnm film. The system developed was tested by capturing imagery of an urban test area. The images obtained were then rectified using photogrammetric techniques. Results obtained were promising with planimetric accuracies of 5 to 1 Om being obtained. From this test it was concluded that for high accuracy applications involving numerous images, use would be made of softcopy photogrammetric software to semi-automatically position and rectify images, while for applications requiring fewer images and lower accuracy, images could be rectified using the simpler technique of assigning GCPs for each image from scanned orthophotos. / Thesis (MSc.)- University of Natal,Pietermaritzburg, 2000.