Contextual classification of multispectral image data: Approximate algorithm

An approximation to a classification algorithm incorporating spatial context information in a general, statistical manner is presented which is computationally less intensive. Classifications that are nearly as accurate are produced

Contextual classification of multispectral image data

There are no author-identified significant results in this report

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis

Utilizing Hierarchical Segmentation to Generate Water and Snow Masks to Facilitate Monitoring Change with Remotely Sensed Image Data

The hierarchical segmentation (HSEG) algorithm is a hybrid of hierarchical step-wise optimization and constrained spectral clustering that produces a hierarchical set of image segmentations. This segmentation hierarchy organizes image data in a manner that makes the image's information content more accessible for analysis by enabling region-based analysis. This paper discusses data analysis with HSEG and describes several measures of region characteristics that may be useful analyzing segmentation hierarchies for various applications. Segmentation hierarchy analysis for generating landwater and snow/ice masks from MODIS (Moderate Resolution Imaging Spectroradiometer) data was demonstrated and compared with the corresponding MODIS standard products. The masks based on HSEG segmentation hierarchies compare very favorably to the MODIS standard products. Further, the HSEG based landwater mask was specifically tailored to the MODIS data and the HSEG snow/ice mask did not require the setting of a critical threshold as required in the production of the corresponding MODIS standard product

Computation of Earth Science Products on Spaceborne Platforms

Spaceborne sensors like NASA's Hyperion hyperspectral imager generate huge data volumes, and several near-term trends indicate that data volumes will only increase. Next-generation hyperspectral missions, such as NASA's Hyperspectral Infrared Imager (HyspIRI), will operate at higher duty cycles and higher data rates, and their users will expect products to be generated from the data in near real time [1]. Barring a sudden advance in satellite downlink capacity, these trends point to a need to process data and generate products onboard the spacecraft. Rather than downlink an entire hyperspectral image cube, onboard processing enables satellites to downlink partial or completed scientific data products, which are often one to two orders of magnitude smaller than the original image. In addition, a satellite with onboard data processing resources and direct broadcast transmission equipment could send data products directly to first responders, research scientists or other users on the ground. Next-generation space-capable data processors will have a combination of reconfigurable gate arrays, digital signal processors and general-purpose CPUs. Correctly programmed and configured, these resources are sufficient to run sophisticated data analysis programs, including hyperspectral image processing algorithms that commonly run on desktop computers [2]. This paper describes how we implemented one such program, the HSEG hierarchical image segmentation algorithm, software commonly used on desktop and parallel processors, on a hardware platform designed to mimic a next-generation space-capable data processor [3]. We also describe our approach to porting the algorithm to and optimizing it for the new platform, and determine the expected performance gains enabled by our design. This extended abstract will describe the HSEG algorithm and hardware platform in greater detail, provide an analysis of the key function within the algorithm that required hardware acceleration, and describe our implementation of that function in hardware

JPSS-1/NOAA-20 VIIRS Early On-Orbit Geometric Performance

The first NOAA/NASA Join Polar Satellite System (JPSS-1) satellite was successfully launched on November 18, 2017,becoming NOAA-20. Instruments on-board the NOAA-20 satellite include the Visible Infrared Imaging RadiometerSuite (VIIRS). This instrument is the second build of VIIRS, with the first flight instrument on-board NASA/NOAASuomi National Polar-orbiting Partnership (SNPP) satellite operating since October 2011. The purpose of these VIIRSinstruments is to continue the long-term measurements of biogeophysical variables for multiple applications includingweather forecasting, rapid response and climate research. The geometric performance of VIIRS is essential to retrievingaccurate biogeophysical variables. This paper describes the early on-orbit geometric performance of the JPSS-1/NOAA-20 VIIRS. It first discusses the on-orbit orbit and attitude performance, a key input needed for accurate geolocation. Itthen discusses the on-orbit geometric characterization and calibration of VIIRS and an initial assessment of thegeometric accuracy. It follows with a discussion of an improvement in the instrument geometric model that correctssmall geometrical artifacts that appear in the along-scan direction. Finally, this paper discusses on-orbit measurements ofthe focal length and the impact of this on the scan-to-scan underlap/overlap

Three human cell types respond to multi-walled carbon nanotubes and titanium dioxide nanobelts with cell-specific transcriptomic and proteomic expression patterns

The growing use of engineered nanoparticles (NPs) in commercial and medical applications raises the urgent need for tools that can predict NP toxicity. We conducted global transcriptome and proteome analyses of three human cell types, exposed to two high aspect ratio NP types, to identify patterns of expression that might indicate high vs. low NP toxicity. Three cell types representing the most common routes of human exposure to NPs, including macrophage like (THP-1), small airway epithelial (SAE), and intestinal (Caco-2/HT29-MTX) cells, were exposed to TiO2 nanobelts (TiO2-NB; high toxicity) and multi-walled carbon nanotubes (MWCNT; low toxicity) at low (10 μg/ml) and high (100 μg/ml) concentrations for 1 and 24 h. Unique patterns of gene and protein expressions were identified for each cell type, with no differentially expressed (p<0.05, 1.5-fold change) genes or proteins overlapping across all three cell types. While unique to each cell-type, the early response was primarily independent of NP type, showing similar expression patterns in response to both TiO2-NB and MWCNT. The early response might therefore indicate a general response to insult. In contrast, the 24 h response was unique to each NP type. The most significantly (p<0.05) enriched biological processes in THP-1 cells indicated TiO2-NB regulation of pathways associated with inflammation, apoptosis, cell cycle arrest, DNA replication stress and genomic instability, while MWCNT regulated pathways indicating increased cell proliferation, DNA repair and anti-apoptosis. These two distinct sets of biological pathways might therefore underlie cellular responses to high and low NP toxicity, respectively

Numerical Investigation of Boundary Conditions for Moving Contact Line Problems

When boundary conditions arising from the usual hydrodynamic assumptions are applied, analyses of dynamic wetting processes lead to a well-known nonintegrable stress singularity at the dynamic contact line, necessitating new ways to model this problem. In this paper, numerical simulations for a set of representative problems are used to explore the possibility of providing material boundary conditions for predictive models of inertialess moving contact line processes. The calculations reveal that up to Capillary number Ca=0.15, the velocity along an arc of radius 10Li (Li is an inner, microscopic length scale! from the dynamic contact line is independent of the macroscopic length scale a for a.103Li , and compares well to the leading order analytical ‘‘modulated-wedge’’ flow field [R. G. Cox, J. Fluid Mech. 168, 169 (1986)] for Capillary number Ca,0.1. Systematic deviations between the numerical and analytical velocity field occur for 0.1168, 169 (1986)] is used as a boundary condition along an arc of radius R=10-2a from the dynamic contact line, agree well with those using two inner slip models for Ca\u3c0.1, with a breakdown at higher Ca. Computations in a cylindrical geometry reveal the role of azimuthal curvature effects on velocity profiles in this vicinity of dynamic contact lines. These calculations show that over an appropriate range of Ca, the velocity field and the meniscus slope in a geometry-independent region can potentially serve as material boundary conditions for models of processes containing dynamic contact lines

Global Food Security Support Analysis Data (GFSAD) at Nominal 1 km (GCAD) Derived from Remote Sensing in Support of Food Security in the Twenty-First Century: Current Achievements and Future Possibilities

The precise estimation of the global agricultural cropland— extents, areas, geographic locations, crop types, cropping intensities, and their watering methods (irrigated or rain-fed; type of irrigation)—provides a critical scientific basis for the development of water and food security policies (Thenkabail et al., 2010, 2011, 2012). By year 2100, the global human population is expected to grow to 10.4 billion under median fertility variants or higher under constant or higher fertility variants (Table 6.1) with over three-quarters living in developing countries and in regions that already lack the capacity to produce enough food. With current agricultural practices, the increased demand for food and nutrition would require about 2 billion hectares of additional cropland, about twice the equivalent to the land area of the United States, and lead to significant increases in greenhouse gas productions associated with agricultural practices and activities (Tillman et al., 2011). For example, during 1960–2010, world population more than doubled from 3 to 7 billion. The nutritional demand of the population also grew swiftly during this period from an average of about 2000 calories per day per person in 1960 to nearly 3000 calories per day per person in 2010. The food demand of increased population along with increased nutritional demand during this period was met by the “green revolution,” which more than tripled the food production, even though croplands decreased from about 0.43 ha per capita to 0.26 ha per capita (FAO, 2009). The increase in food production during the green revolution was the result of factors such as: (1) expansion of irrigated croplands, which had increased in 2000 from 130 Mha in the 1960s to between 278 Mha (Siebert et al., 2006) and 467 Mha (Thenkabail et al., 2009a,b,c), with the larger estimate due to consideration of cropping intensity; (2) increase in yield and per capita production of food (e.g., cereal production from 280 to 380 kg/person and meat from 22 to 34 kg/person (McIntyre, 2008); (3) new cultivar types (e.g., hybrid varieties of wheat and rice, biotechnology); and (4) modern agronomic and crop management practices (e.g., fertilizers, herbicide, pesticide applications)..

Effect of acute copper sulfate exposure on olfactory responses to amino acids and pheromones in goldfish (Carassius auratus)

Exposure of olfactory epithelium to environmentally relevant concentrations of copper disrupts olfaction in fish. To examine the dynamics of recovery at both functional and morphological levels after acute copper exposure, unilateral exposure of goldfish olfactory epithelia to 100 μM CuSO4 (10 min) was followed by electro-olfactogram (EOG) recording and scanning electron microscopy. Sensitivity to amino acids (L-arginine and L-serine), generally considered food-related odorants, recovered most rapidly (three days), followed by that to catecholamines(3-O-methoxytyramine),bileacids(taurolithocholic acid) and the steroid pheromone, 17,20 -dihydroxy-4-pregnen- 3-one 20-sulfate, which took 28 days to reach full recovery. Sensitivity to the postovulatory pheromone prostaglandin F2R had not fully recovered even at 28 days. These changes in sensitivity were correlated with changes in the recovery of ciliated and microvillous receptor cell types. Microvillous cells appeared largely unaffected by CuSO4 treatment. Cilia in ciliated receptor neurones, however, appeared damaged one day post-treatment and were virtually absent after three days but had begun to recover after 14 days. Together, these results support the hypothesis that microvillous receptor neurones detect amino acids whereas ciliated receptor neurones were not functional and are responsible for detection of social stimuli (bile acidsandpheromones).Furthermore, differences in sensitivity to copper may be due to different transduction pathways in the different cell types