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Proceedings Paper

Learning sparse discriminative representations for land cover classification in the Arctic
Author(s): Daniela I Moody; Steven P. Brumby; Joel C. Rowland; Chandana Gangodagamage
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Paper Abstract

Neuroscience-inspired machine vision algorithms are of current interest in the areas of detection and monitoring of climate change impacts, and general Land Use/Land Cover classification using satellite image data. We describe an approach for automatic classification of land cover in multispectral satellite imagery of the Arctic using sparse representations over learned dictionaries. We demonstrate our method using DigitalGlobe Worldview-2 8-band visible/near infrared high spatial resolution imagery of the MacKenzie River basin. We use an on-line batch Hebbian learning rule to build spectral-textural dictionaries that are adapted to this multispectral data. We learn our dictionaries from millions of overlapping image patches and then use a pursuit search to generate sparse classification features. We explore unsupervised clustering in the sparse representation space to produce land-cover category labels. This approach combines spectral and spatial textural characteristics to detect geologic, vegetative, and hydrologic features. We compare our technique to standard remote sensing algorithms. Our results suggest that neuroscience-based models are a promising approach to practical pattern recognition problems in remote sensing, even for datasets using spectral bands not found in natural visual systems.

Paper Details

Date Published: 19 October 2012
PDF: 14 pages
Proc. SPIE 8514, Satellite Data Compression, Communications, and Processing VIII, 85140Q (19 October 2012); doi: 10.1117/12.930182
Show Author Affiliations
Daniela I Moody, Los Alamos National Lab. (United States)
Steven P. Brumby, Los Alamos National Lab. (United States)
Joel C. Rowland, Los Alamos National Lab. (United States)
Chandana Gangodagamage, Los Alamos National Lab. (United States)

Published in SPIE Proceedings Vol. 8514:
Satellite Data Compression, Communications, and Processing VIII
Bormin Huang; Antonio J. Plaza; Carole Thiebaut, Editor(s)

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