“Andrew is a very versatile developer with a lot of good ideas. He was always eager to dive in and learn different areas of the platform across a variety of languages. We really enjoyed having Andrew on the team.”
About
Passionate engineer and lifelong learner.
Activity
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Congrats to Cinder cofounder and Chief Strategy Officer Brian F. for his keynote yesterday at the Terrorism and Social Media Conference at Swansea…
Congrats to Cinder cofounder and Chief Strategy Officer Brian F. for his keynote yesterday at the Terrorism and Social Media Conference at Swansea…
Liked by Andrew Gershman
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We are excited that Cinder will be returning to TrustCon as a Champion sponsor! We are deeply grateful for their commitment to the trust and safety…
We are excited that Cinder will be returning to TrustCon as a Champion sponsor! We are deeply grateful for their commitment to the trust and safety…
Liked by Andrew Gershman
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Our Pride stickers designed by Jon Hsiung have been such a hit that we are bringing them to #TrustCon! Stop by our exhibition on Monday or any of our…
Our Pride stickers designed by Jon Hsiung have been such a hit that we are bringing them to #TrustCon! Stop by our exhibition on Monday or any of our…
Liked by Andrew Gershman
Experience
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Education
Publications
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Spectral Matching Accuracy in Processing Hyperspectral Data
Proceedings IEEE ISSCS
We investigated the accuracy of several spectral metrics when used for matching in hyperspectral imagery. Spectral matching refers to measuring the similarity among spectra. Two spectra are similar when the spectra distance between them is small and dissimilar otherwise. Spectral matching is used in many areas such as target detection and spectra classification. At the heart of spectral matching lay the distance measure used and the threshold value differentiating between similar and…
We investigated the accuracy of several spectral metrics when used for matching in hyperspectral imagery. Spectral matching refers to measuring the similarity among spectra. Two spectra are similar when the spectra distance between them is small and dissimilar otherwise. Spectral matching is used in many areas such as target detection and spectra classification. At the heart of spectral matching lay the distance measure used and the threshold value differentiating between similar and dissimilar. Frequent choices for such measures are spectral angle (SA) and spectral information divergence (SID) since they provide a limited range for threshold values. In addition, two new measures spectral correlation angle (SGA) and spectral gradient angle (SGA) are developed. Next, we suggest an alternative measure developed by using a normalized Euclidean distance (NED). To test the accuracy of the measures we used target information extracted from HYDICE data, and assessment tools such as the relative spectral discriminatory probability and the relative spectral discriminatory entropy. Experimental results suggest that NED outperforms SA, SCA and SGA and is relatively equivalent to SID. Given the reduction in computation time, NED constitutes an attractive measure to be used in spectra matching.
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English
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