“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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Does your Trust & Safety team struggle to find engineering support in your organization? Or do you have all the engineering support but want to learn…
Does your Trust & Safety team struggle to find engineering support in your organization? Or do you have all the engineering support but want to learn…
Liked by Andrew Gershman
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TrustCon! Come on by the Cinder x Accel happy hour! Wait, did you get caught by the waitlist?? No fear. Message me or one of my teammates, we’ll…
TrustCon! Come on by the Cinder x Accel happy hour! Wait, did you get caught by the waitlist?? No fear. Message me or one of my teammates, we’ll…
Liked by Andrew Gershman
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LET'S GET READY TO RUMBLE!!!!!!!!!!!!! Happy TrustCon to all who celebrate. If you're at TrustCon stop by the Cinder booth for some Cinder Blocks…
LET'S GET READY TO RUMBLE!!!!!!!!!!!!! Happy TrustCon to all who celebrate. If you're at TrustCon stop by the Cinder booth for some Cinder Blocks…
Liked by Andrew Gershman
Experience
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.
Other authors
Languages
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English
Native or bilingual proficiency
Recommendations received
9 people have recommended Andrew
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Can’t wait to see you all in a couple weeks! 🙌 Let us know if we'll be seeing you there.
Can’t wait to see you all in a couple weeks! 🙌 Let us know if we'll be seeing you there.
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At Cinder, we’re thrilled to serve as inaugural contributors of the Carnegie Mellon University CyLab Venture network. We're helping to build a…
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