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Synthetic aperture radar (SAR) can penetrate clouds, rendering these data particularly useful for mapping land cover and land use in tropical areas. In this study, we leverage the image processing and analysis platform built at Descartes Labs to analyze a time-series of Sentinel-1 SAR data acquired during the 2014 – 2015 growing season across the Vietnamese Mekong River Delta, a region that is dominated...
The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. Cloud computing and storage, combined with recent advances in machine learning,...
The increase in number of deployed satellite constellations and the improvement in sensing capabilities have led to large volumes of data with a wide range of temporal and spatial coverage. The data analysis capability, however, has been lagging, and has historically focused on single-sensor individual images. We present results from an ongoing effort to develop satellite imagery analysis tools that...
Exponential growth in data streams and discovery power delivered by modern time-domain imaging surveys creates a pressing need for variability extraction algorithms that are both fully automated and highly reliable. The current state of the art methods based on image differencing are limited by the fact that for every real variable source the algorithm returns a large number of bogus “detections”...
Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian...
Ongoing research at Los Alamos National Laboratory (LANL) studies the Earth's radiofrequency (RF) background utilizing satellite-based RF observations of terrestrial lightning. The Fast On-orbit Recording of Transient Events (FORTE) satellite provided a rich satellite lightning database, that has been previously used for some event classification. We now develop and implement new event classification...
Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of current interest in the areas of climate change monitoring, change detection, and 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...
We assess the performance of a sparse classification approach for radiofrequency (RF) transient signals using dictionaries adapted to the data. We explore two approaches: pursuit-type decompositions over analytical, over-complete dictionaries, and dictionaries learned directly from data. Pursuit-type decompositions over analytical, over-complete dictionaries yield sparse representations by design...
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