Rick Chartrand - Academia.edu (original) (raw)

Papers by Rick Chartrand

2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), 2017

Synthetic aperture radar (SAR) can penetrate clouds, rendering these data particularly useful for... more 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 by rice paddy agriculture. Rice is a staple food for the majority of the global population, but production is threatened by expanding urban areas, rising temperatures, and encroaching sea levels. Most of the world's rice is grown in the monsoonal tropics, and frequent cloud cover makes monitoring the landscape challenging. Here, we illustrate how the unique phenology of rice is captured with SAR data to accurately map annual rice paddy extent, and we show how the method can be extended to also determine the amount of rice grown during each growing period within a season.

2016 Seventh International Workshop on Data-Intensive Computing in the Clouds (DataCloud), 2016

We present our experiences using cloud computing to support data-intensive analytics on satellite... more We present our experiences using cloud computing to support data-intensive analytics on satellite imagery for commercial applications. Drawing from our background in highperformance computing, we draw parallels between the early days of clustered computing systems and the current state of cloud computing and its potential to disrupt the HPC market. Using our own virtual file system layer on top of cloud remote object storage, we demonstrate aggregate read bandwidth of 230 gigabytes per second using 512 Google Compute Engine (GCE) nodes accessing a USA multi-region standard storage bucket. This figure is comparable to the best HPC storage systems in existence. We also present several of our application results, including the identification of field boundaries in Ukraine, and the generation of a global cloud-free base layer from Landsat imagery.

AGU Fall Meeting Abstracts, Dec 1, 2018

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The European Space Agency’s Sentinel 1 satellite acquires global synthetic aperture radar (SAR) d... more The European Space Agency’s Sentinel 1 satellite acquires global synthetic aperture radar (SAR) data, making it particularly well-suited for analyzing tropical regions that may be covered in clouds and therefore concealed from optical data. Here, we focus our attention on rice, a predominant crop in the tropics, and leverage Sentinel 1 data to identify field boundaries, classify fields as rice or not rice, and estimate the number of times each rice field is harvested during a year. Using the Descartes Labs Platform to conduct this analysis allows us to scale our models to run across Asia, providing a region-wide analysis of rice extent and management.

IEEE International Conference on Plasma Science, 2009

Peak core temperature is an important measure of implosion performance, and will be a critical di... more Peak core temperature is an important measure of implosion performance, and will be a critical diagnostic of ignition implosion performance at the National Ignition Facility because peak temperatures must attain high values in order to form the hot-spot spark that ignites the main mass of deuterium-tritium fuel. We plan to measure peak core temperatures, resolved in space and time, using

2007 16th IEEE International Pulsed Power Conference, 2007

A Series of dynamic friction experiments has been conducted at the Atlas Pulsed Power Facility. P... more A Series of dynamic friction experiments has been conducted at the Atlas Pulsed Power Facility. Pulsed currents in excess of 21 MAmps were delivered to a cylindrical liner in about 15 ms. The liner was accelerated to km/s velocities and symmetrically impacted a hollow Ta/Al/Ta target. Due to the shock speed difference in Ta and Al, sliding velocities of almost

IEEE Signal Processing Letters, 2007

IEEE Journal of Selected Topics in Signal Processing, 2010

IEEE Transactions on Signal Processing, 2012

The ℓ^0 minimization of compressed sensing is often relaxed to ℓ^1, which yields easy computation... more The ℓ^0 minimization of compressed sensing is often relaxed to ℓ^1, which yields easy computation using the shrinkage mapping known as soft thresholding, and can be shown to recover the original solution under certain hypotheses. Recent work has derived a general class of shrinkages and associated nonconvex penalties that better approximate the original ℓ^0 penalty and empirically can recover the original solution from fewer measurements. We specifically examine p-shrinkage and firm thresholding. In this work, we prove that given data and a measurement matrix from a broad class of matrices, one can choose parameters for these classes of shrinkages to guarantee exact recovery of the sparsest solution. We further prove convergence of the algorithm iterative p-shrinkage (IPS) for solving one such relaxed problem.

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019

We consider the problem of unwrapping the phase of two-dimensional interferograms, and adopt a kn... more We consider the problem of unwrapping the phase of two-dimensional interferograms, and adopt a known formulation as a sparse optimization problem. Many algorithms have been developed for solving sparse optimization problems that occur in the field of compressive sensing; in this work, we adapt one such algorithm for use in the unwrapping problem. The result is an unwrapping algorithm that gives very similar results to those of existing algorithms, but that is simpler, more reliable, and more computationally efficient.

We present a new, simple, and elegant algorithm for computing the optimal mapping for the Monge-K... more We present a new, simple, and elegant algorithm for computing the optimal mapping for the Monge-Kantorovich problem with quadratic cost. The method arises from a reformulation of the dual problem into an unconstrained minimization of a convex, continuous functional, for which the derivative can be explicitly found. The Monge-Kantorovich problem has applications in many fields; examples from image warping and medical imaging are shown.

The global phenomenon of forest degradation is a pressing issue with severe implications for clim... more The global phenomenon of forest degradation is a pressing issue with severe implications for climate stability and biodiversity protection. In this work we generate Bayesian updating deforestation detection (BUDD) algorithms by incorporating Sentinel-l backscatter and interferometric coherence with Sentinel-2 normalized vegetation index data. We show that the algorithm provides good performance in validation AOIs. We compare the effectiveness of different combinations of the three data modalities as inputs into the BUDD algorithm and compare against existing benchmarks based on optical imagery.

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII

The increase in performance, availability, and coverage of multispectral satellite sensor constel... more The increase in performance, availability, and coverage of multispectral satellite sensor constellations has led to a drastic increase in data volume and data rate. 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. The data analysis capability, however, has lagged behind storage and compute developments, and has traditionally focused on individual scene processing. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and can scale with the high-rate and dimensionality of imagery being collected. We investigate and compare the performance of pixel-level crop identification using tree-based classifiers and its dependence on both temporal and spectral features. Classification performance is assessed using as ground-truth Cropland Data Layer (CDL) crop masks generated by the US Department of Agriculture (USDA). The CDL maps contain 30m spatial resolution, pixel-level labels for around 200 categories of land cover, but are however only available post-growing season. The analysis focuses on McCook county in South Dakota and shows crop classification using a temporal stack of Landsat 8 (L8) imagery over the growing season, from April through October. Specifically, we consider the temporal L8 stack depth, as well as different normalized band difference indices, and evaluate their contribution to crop identification. We also show an extension of our algorithm to map corn and soy crops in the state of Mato Grosso, Brazil.