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.
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Showing 1 to 20 of 223 for “"Remote sensing data"”.
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Robust compression of multispectral remote sensing data
… coding algorithms for multispectral remote sensing data. Although many efficient non-reversible coding algorithms have been proposed for such data, their application is often limited due to the risk of excessively degrading the data if, for example, changes in sensor characteristics …
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Improved remote sensing data analysis using neural networks
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
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Autonomous Vehicle Path Planning with Remote Sensing Data
… autonomous ground vehicle with minimal a-priori data is still very much an open problem. Previous research has demonstrated that least cost paths generated from aerial LIDAR and GIS data could play a role in automatically determining suitable routes over otherwise unknown terrain. However, most …
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Scale-recursive estimation of precipitation using remote sensing data
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1996.
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Advanced regression and detection methods for remote sensing data analysis
Nowadays the analysis of remote sensing data for environmental monitoring is fundamental to understand the local and global Earth dynamics. In this context, the main goal of this thesis is to present novel signal processing methods for the estimation of biophysical parameters and for the analysis …
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Manifold learning based spectral unmixing of hyperspectral remote sensing data
… 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 …
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Improving Retrievals of Crop Vegetation Parameters from Remote Sensing Data
… the interaction of these factors, the amount of data necessary to develop and utilize models to accurately predict the performance of agricultural systems at an operational scale is large. Satellite remote sensing provides the potential to vastly increase the amount of data available for …
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Machine Learning and Data Fusion of Simulated Remote Sensing Data
… different ocean environments and simulated remote sensing platforms is conducted to generate a preliminary data set that is used for training and testing neural network--based ship wake detection models. Several different model architectures are trained and tested, which are able to provide …
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Marine macrophyte monitoringenabling scalability through remote sensing data and cloud computing
La introducción de especies exóticas invasoras, el cambio climático, las modificaciones de hábitats marinos y la tendencia hacia la eutrofización costera, ya han afectado a la distribución y salud de las especies de macrófitos marinos, causando importantes pérdidas de biodiversidad en todo el …
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Land cover mapping through optimizing remote sensing data for SVM classification
… digital image processing and more recently remote sensing. The three commonly used SVMs include linear, polynomial and radial basis function (i.e. Gaussian) classifiers.
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Variational assimilation of remote sensing data for land surface hydrologic applications
… from low-frequency (L-band) passive microwave remote sensing observations using weak-constraint variational data assimilation. We extend the iterated indirect representer method, which is based on the adjoint of the hydrologic model, to suit our application. The four-dimensional (space and …
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Determining land use change and desertification in China using remote sensing data
… land cover change. The climate and population data is resampled to an uniform 0.5' scale and converted into qualitative, data before statistical testing. This project tests if land cover change, a more difficult indicator to measure, can be predicted by analyzing trends in vegetation, …
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Using Remote Sensing Data to Predict Habitat Occupancy of Pine Savanna Bird Species
… satellite imagery. Sentinel-2 satellite imagery data provides an instantaneous snapshot of habitat quality at a high resolution and across a large geographic area, which may make it more efficient than traditional, ground-based vegetation surveying. Thus, the objectives of my research were to 1) …
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Advanced Techniques For Prediction of Forest Above Ground Biomass Using Satellite Remote Sensing Data
… forest management and planning. The use of SRS data has recently increased for AGB prediction due to their large footprint and low cost availability. There are various limitations and problems with SRS data that require innovative and effective solutions for large-scale AGB mapping. This thesis …
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Insights into marine biological responses to Icelandic glacial outburst floods from remote sensing data
… the Gígjukvísl, Skaftá, and Kúðafljót estuaries. Remote sensing data integrated in situ, observational, and modelled data to analyse sea surface salinity (1/8° resolution), temperature (0.05° resolution), and observational chlorophyll-a data (1 km² resolution). Covering 11 jökulhlaups in …
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Vegetation and Forest Fire Dynamics in Alberta: A Ground and Remote Sensing Data Analysis
… the lens of climate change. By synthesizing data from remote sensing, climate records, and fire databases, the study reveals the intricate relationships between vegetation cover changes and climatic factors throughout 2001–2022. It highlights the significant lead and lag times between the …
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Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics
… light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale …
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Tracking Dust Plumes and Identifying Source Areas Using Spatiotemporal Clustering of Remote Sensing Data
Traditionally, studies on dust relied on polar-orbiting satellites whose limited tempo-ral coverage does not offer a detailed picture of how dust plumes evolve and change over time. To address this, we develop a method to identify and track individual dust plumes via hourly images from the Meteosat …
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