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FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding
IEEE Geoscience and Remote Sensing Letters, Volume: 12, Issue: 1, Pages: 18 - 22
Swansea University Author: Mark Jones
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DOI (Published version): 10.1109/LGRS.2014.2324631
Abstract
Hyperspectral image visualization reduces color bands to three, but prevailing linear methods fail to address data characteristics, and nonlinear embeddings are computationally demanding. Qualitative evaluation of embedding is also lacking. We propose faithful stochastic proximity embedding (FSPE),...
Published in: | IEEE Geoscience and Remote Sensing Letters |
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2014
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URI: | https://cronfa.swan.ac.uk/Record/cronfa18053 |
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2019-06-21T12:45:35.1032723 v2 18053 2014-06-23 FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 2014-06-23 SCS Hyperspectral image visualization reduces color bands to three, but prevailing linear methods fail to address data characteristics, and nonlinear embeddings are computationally demanding. Qualitative evaluation of embedding is also lacking. We propose faithful stochastic proximity embedding (FSPE), which is a scalable and nonlinear dimensionality reduction method. FSPE considers the nonlinear characteristics of spectral signatures, yet it avoids the costly computation of geodesic distances that are often required by other nonlinear methods. Furthermore, we employ a pixelwise metric that measures the quality of hyperspectral image visualization at each pixel. FSPE outperforms the state-of-art methods by at least 12% on average and up to 25% in the qualitative measure. An implementation on graphics processing units is two orders of magnitude faster than the baseline. Our method opens the path to high-fidelity and real-time analysis of hyperspectral images. Journal Article IEEE Geoscience and Remote Sensing Letters 12 1 18 22 20 6 2014 2014-06-20 10.1109/LGRS.2014.2324631 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2019-06-21T12:45:35.1032723 2014-06-23T12:43:41.3024372 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Safa A. Najim 1 Ik Soo Lim 2 Peter Wittek 3 Mark Jones 0000-0001-8991-1190 4 0018053-15042015121236.pdf FSPE.pdf 2015-04-15T12:12:36.1570000 Output 1168369 application/pdf Version of Record true 2015-04-14T00:00:00.0000000 true |
title |
FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding |
spellingShingle |
FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding Mark Jones |
title_short |
FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding |
title_full |
FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding |
title_fullStr |
FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding |
title_full_unstemmed |
FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding |
title_sort |
FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding |
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2e1030b6e14fc9debd5d5ae7cc335562 |
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2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones |
author |
Mark Jones |
author2 |
Safa A. Najim Ik Soo Lim Peter Wittek Mark Jones |
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IEEE Geoscience and Remote Sensing Letters |
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10.1109/LGRS.2014.2324631 |
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description |
Hyperspectral image visualization reduces color bands to three, but prevailing linear methods fail to address data characteristics, and nonlinear embeddings are computationally demanding. Qualitative evaluation of embedding is also lacking. We propose faithful stochastic proximity embedding (FSPE), which is a scalable and nonlinear dimensionality reduction method. FSPE considers the nonlinear characteristics of spectral signatures, yet it avoids the costly computation of geodesic distances that are often required by other nonlinear methods. Furthermore, we employ a pixelwise metric that measures the quality of hyperspectral image visualization at each pixel. FSPE outperforms the state-of-art methods by at least 12% on average and up to 25% in the qualitative measure. An implementation on graphics processing units is two orders of magnitude faster than the baseline. Our method opens the path to high-fidelity and real-time analysis of hyperspectral images. |
published_date |
2014-06-20T03:21:03Z |
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1763750616962695168 |
score |
11.016235 |