Open Access. Powered by Scholars. Published by Universities.®

Social and Behavioral Sciences Commons

Open Access. Powered by Scholars. Published by Universities.®

Electrical and Computer Engineering

PDF

Purdue University

Remote sensing

Articles 1 - 2 of 2

Full-Text Articles in Social and Behavioral Sciences

High Wind Alerts: A System Created With Observations From The X-Band Teaching And Research Radar, Lauren Warner Aug 2020

High Wind Alerts: A System Created With Observations From The X-Band Teaching And Research Radar, Lauren Warner

The Journal of Purdue Undergraduate Research

Following the August 13, 2011, Indiana State Fair stage collapse tragedy, caused by a wind gust from an approaching thunderstorm, Purdue University enforced a wind speed restriction of 30 mph (13 m s-1) for tents at outdoor events. During these events, volunteers stand outside with handheld anemometers, measuring and reporting when the wind speeds exceed this limit. In this study, we report testing of a new system to automate high-wind alerts based on observations from a Doppler radar, the X-band Teaching and Research Radar (XTRRA), near Purdue’s campus. XTRRA scans over campus at low elevations approximately every 5 minutes. Using …


Manifold Learning Based Spectral Unmixing Of Hyperspectral Remote Sensing Data, Jun-Hwa Chi Oct 2013

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 …