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Full-Text Articles in Engineering
Estimating The Water Quality Condition Of River And Lake Water In The Midwestern United States From Its Spectral Characteristics, Jing Tan
Open Access Dissertations
This study focuses on developing/calibrating remote sensing algorithms for water quality retrieval in Midwestern rivers and lakes. In the first part of this study, the spectral measurements collected using a hand-held spectrometer as well as water quality observations for the Wabash River and its tributary the Tippecanoe River in Indiana were used to develop empirical models for the retrieval of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using a subset of the field measurements with the …
Automated Segmentation, Detection And Fitting Of Piping Elements From Terrestrial Lidar Data, Yun-Ting Su
Automated Segmentation, Detection And Fitting Of Piping Elements From Terrestrial Lidar Data, Yun-Ting Su
Open Access Dissertations
Since the invention of light detection and ranging (LIDAR) in the early 1960s, it has been adopted for use in numerous applications, from topographical mapping with airborne LIDAR platforms to surveying of urban sites with terrestrial LIDAR systems. Static terrestrial LIDAR has become an especially effective tool for surveying, in some cases replacing traditional techniques such as electronic total stations and GPS methods. Current state-of-the-art LIDAR scanners have very fine spatial resolution, generating precise 3D point cloud data with millimeter accuracy. Therefore, LIDAR data can provide 3D details of a scene with an unprecedented level of details. However, automated exploitation …
Manifold Learning Based Spectral Unmixing Of Hyperspectral Remote Sensing Data, Jun-Hwa Chi
Manifold Learning Based Spectral Unmixing Of Hyperspectral Remote Sensing Data, Jun-Hwa Chi
Open Access Dissertations
Nonlinear mixing effects inherent in hyperspectral data are not properly represented in linear spectral unmixing models. Although direct nonlinear unmixing models provide capability to capture nonlinear phenomena, they are difficult to formulate and the results are not always generalizable. Manifold learning based spectral unmixing accommodates nonlinearity in the data in the feature extraction stage followed by linear mixing, thereby incorporating some characteristics of nonlinearity while retaining advantages of linear unmixing approaches. Since endmember selection is critical to successful spectral unmixing, it is important to select proper endmembers from the manifold space. However, excessive computational burden hinders development of manifolds for …