Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a Hebbian learning rule to build undercomplete spectral-textural dictionaries that are adapted to the data. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using our CoSA algorithm: unsupervised Clustering of Sparse Approximations. We demonstrate our method using multispectral Worldview-2 data from three Arctic study areas: Barrow, Alaska; the Selawik River, Alaska; and a watershed near the Mackenzie River delta in northwest Canada. Our goal is to develop a robust classification methodology that will allow for the automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and geomorphic characteristics. To interpret and assign land cover categories to the clusters we both evaluate the spectral properties of the clusters and compare the clusters to both field- and remote sensing-derived classifications of landscape attributes. Our work suggests that neuroscience-based models are a promising approach to practical pattern recognition problems in remote sensing.
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.
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