Change detection in Arctic satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

Daniela I. Moody; Cathy J. Wilson; Joel C. Rowland; Garrett L. Altmann

doi:10.1117/12.2177590

18 June 2015 Change detection in Arctic satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

Daniela I. Moody, Cathy J. Wilson, Joel C. Rowland, Garrett L. Altmann

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Proceedings Volume 9494, Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX; 94940W (2015) https://doi.org/10.1117/12.2177590
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

Advanced pattern recognition and computer vision algorithms are of great interest for landscape characterization, change detection, and change monitoring in satellite imagery, in support of global climate change science and modeling. We present results from an ongoing effort to extend neuroscience-inspired models for feature extraction to the environmental sciences, and we demonstrate our work using Worldview-2 multispectral satellite imagery. We use a Hebbian learning rule to derive multispectral, multiresolution dictionaries directly from regional satellite normalized band difference index data. These feature dictionaries are used to build sparse scene representations, from which we automatically generate land cover labels via our CoSA algorithm: Clustering of Sparse Approximations. These data adaptive feature dictionaries use joint spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologic features. Land cover labels are estimated in example Worldview-2 satellite images of Barrow, Alaska, taken at two different times, and are used to detect and discuss seasonal surface changes. Our results suggest that an approach that learns from both spectral and spatial features is promising for practical pattern recognition problems in high resolution satellite imagery.

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Daniela I. Moody, Cathy J. Wilson, Joel C. Rowland, and Garrett L. Altmann "Change detection in Arctic satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries", Proc. SPIE 9494, Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX, 94940W (18 June 2015); https://doi.org/10.1117/12.2177590

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KEYWORDS

Associative arrays

Satellites

Earth observing sensors

Satellite imaging

Spatial resolution

Vegetation

Image resolution

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