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Full-Text Articles in Engineering
Improving Detection Of Dim Targets: Optimization Of A Moment-Based Detection Algorithm, Shannon R. Young
Improving Detection Of Dim Targets: Optimization Of A Moment-Based Detection Algorithm, Shannon R. Young
Theses and Dissertations
Wide area motion imagery (WAMI) sensor technology is advancing rapidly. Increases in frame rates and detector array sizes have led to a dramatic increase in the volume of data that can be acquired. Without a corresponding increase in analytical manpower, much of these data remain underutilized. This creates a need for fast, automated, and robust methods for detecting dim, moving signals of interest. Current approaches fall into two categories: detect-before-track (DBT) and track-before-detect (TBD) methods. The DBT methods use thresholding to reduce the quantity of data to be processed, making real time implementation practical but at the cost of the …
Remote Sensing Of Soil Moisture Using S-Band Signals Of Opportunity: Model Development And Experimental Validation, Marvin Jesse, Benjamin Nold, James L. Garrison
Remote Sensing Of Soil Moisture Using S-Band Signals Of Opportunity: Model Development And Experimental Validation, Marvin Jesse, Benjamin Nold, James L. Garrison
The Summer Undergraduate Research Fellowship (SURF) Symposium
Root zone soil moisture (RZSM) is a vital aspect in meteorology, hydrology, and agriculture. There are currently some methods in passive and active remote sensing at L-band, but these methods are limited to a sensing depth of approximately 10 cm. Observing RZSM (water in the top meter of soil) will require lower frequencies, thus presenting significant difficulties for a spaceborne instrument, because of the required antenna size, the presence of radio-frequency interference (RFI), and competition for spectrum allocations (in the case of active radar). Bistatic radar using Signal of Opportunity (SoOp) (e.g. digital satellite transmitters) provides an opportunity for remote …
Remote Sensing Using I-Band And S-Band Signals Of Opportunity, Kadir Efecik, Benjamin R. Nold, James L. Garrison
Remote Sensing Using I-Band And S-Band Signals Of Opportunity, Kadir Efecik, Benjamin R. Nold, James L. Garrison
The Summer Undergraduate Research Fellowship (SURF) Symposium
Measurement of soil moisture, especially the root zone soil moisture, is important in agriculture, meteorology, and hydrology. Root zone soil moisture is concerned with the first meter down the soil. Active and passive remote sensing methods used today utilizing L-band(1-2GHz) are physically limited to a sensing depth of about 5 cm or less. To remotely sense the soil moisture in the deeper parts of the soil, the frequency should be lowered. Lower frequencies cannot be used in active spaceborne instruments because of their need for larger antennas, radio frequency interference (RFI), and frequency spectrum allocations. Ground-based passive remote sensing using …
Statistical Analysis And Comparison Of Optical Classification Of Atmospheric Aerosol Lidar Data, Kwasi Gyening Afrifa
Statistical Analysis And Comparison Of Optical Classification Of Atmospheric Aerosol Lidar Data, Kwasi Gyening Afrifa
Electrical & Computer Engineering Projects for D. Eng. Degree
This dissertation presents a new study for the analysis and classification of atmospheric aerosols in remote sensing LIDAR data. Information on particle size and associated properties are extracted from these remote sensing atmospheric data which are collected by a ground-based LIDAR system. This study first considers optical LIDAR parameter-based classification methods for clustering and classification of different types of harmful aerosol particles in the atmosphere. Since accurate methods for aerosol prediction behaviors are based upon observed data, computational approaches must overcome design limitations, and also consider appropriate calibration and estimation accuracy. Consequently, two statistical methods based on generalized linear models …
Sar Image Time-Series Analysis Framework Using Morphological Operators And Global And Local Information-Based Linear Discriminant Analysis, Ufuk Sakarya, Caner Demi̇rpolat
Sar Image Time-Series Analysis Framework Using Morphological Operators And Global And Local Information-Based Linear Discriminant Analysis, Ufuk Sakarya, Caner Demi̇rpolat
Turkish Journal of Electrical Engineering and Computer Sciences
Fusion of spectral, spatial, and temporal information is an effective method used in many satellite remote sensing applications. On the other hand, one drawback of this fusion is an increase in complexity. In this paper, we focus on developing a fast and well-performed classification method for agricultural crops using time-series SAR data. In order to achieve this, a novel two-stage approach is proposed. In the first stage, a high-dimensional feature space is obtained using time-series dual-pol SAR data and morphological operators. Spectral, spatial, and temporal information is combined into a single high-dimensional feature space. In the second stage, a dimension …
Automated Tree-Level Forest Quantification Using Airborne Lidar, Hamid Hamraz
Automated Tree-Level Forest Quantification Using Airborne Lidar, Hamid Hamraz
Theses and Dissertations--Computer Science
Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree …
Application Of Remote Sensing And Machine Learning Modeling To Post-Wildfire Debris Flow Risks, Priscilla Addison
Application Of Remote Sensing And Machine Learning Modeling To Post-Wildfire Debris Flow Risks, Priscilla Addison
Dissertations, Master's Theses and Master's Reports
Historically, post-fire debris flows (DFs) have been mostly more deadly than the fires that preceded them. Fires can transform a location that had no history of DFs to one that is primed for it. Studies have found that the higher the severity of the fire, the higher the probability of DF occurrence. Due to high fatalities associated with these events, several statistical models have been developed for use as emergency decision support tools. These previous models used linear modeling approaches that produced subpar results. Our study therefore investigated the application of nonlinear machine learning modeling as an alternative. Existing models …