Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 8 of 8 for “"Hyperspectral dataset"”.
-
Time Series Analysis of Vegetation Change using Hyperspectral and Multispectral Data
… Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset, also collected during 2011. These data were used along with additional reflectance-corrected multispectral datasets (IKONOS from 2007 and QuickBird from 2006 and 2009) to create vegetation classification maps using both …
-
Improved hyperspectral classification of vegetation through generative deep learning models.
Early studies into hyperspectral reflectance demonstrated that the spectra of different plants have the potential for taxonomic discrimination and classification, though this came with the caveat that misidentification was a frequent impediment as a result of small sample sizes, inter-class …
-
Predicting Spatial Variability of Soil Organic Carbon in Delmarva Bays
… reduced 50 terrain-related values to three datasets of 16, 11, and 7 variables. Five types of non-linear models were examined: Generalized Linear Mondel (GLM) ridge, GLM LASSO, Generalized Additive Model (GAM) non-penalized, GAM cubic splice, and partial least-squares regression. Carbon …
-
Evaluating Post-fire Vegetation Recovery in Canadian Mixed Prairie Using Remote Sensing Approaches
… sensing approaches. Biophysical parameters and hyperspectral reflectances were collected through field surveys conducted one year prior to the fire as well as five continuous years post-fire at growing seasons. These data were processed into burned and unburned samples followed by significance …
-
Terrain characterization for site selection and preparation
… accuracy by reducing the dimensionality of a hyperspectral dataset to resemble a standard multispectral dataset. The ET model produced better estimations of SMC when trained with the reduced dimensionality (RD) input set and concatenated multispectral (CM) set – obtaining an increase of 1.3% …
-
Development of Ground-Level Hyperspectral Image Datasets and Analysis Tools, and their use towards a Feature Selection based Sensor Design Method for Material Classification
… Separately, in the field of remote sensing, the hyperspectral camera has been used to perform classification tasks on natural and man-made objects from typically aerial or satellite platforms. Hyperspectral data is characterized by a very fine spectral resolution, resulting in a significant …
-
Deep learning applications in hyperspectral imaging for agriculture: image reconstruction and model design for quality prediction
Non-invasive techniques, such as hyperspectral imaging (HSI), are crucial for analyzing the detailed chemical and structural composition of agricultural products. By capturing both spectral and spatial information simultaneously, HSI enables advanced analysis of key quality attributes in …
-
Unraveling Complexity: Panoptic Segmentation in Cellular and Space Imagery
… heavily on the availability of extensive labeled datasets. Collecting large amounts of labeled data poses a significant financial burden, particularly in specialized fields like medical imaging and remote sensing, where annotation requires expert knowledge. To address this challenge, various …