High-resolution infrared molecular hydrogen images and optical images of Herbig-Haro object 43

1988 ◽  
Vol 334 ◽  
pp. L99 ◽  
Author(s):  
Richard D. Schwartz ◽  
Donald G. Jennings ◽  
Peredur M. Williams ◽  
Martin Cohen
Keyword(s):  
High Resolution ◽  
Molecular Hydrogen ◽  
Optical Images

scholarly journals SAR AND OBLIQUE AERIAL OPTICAL IMAGE FUSION FOR URBAN AREA IMAGE SEGMENTATION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 639-642 ◽  
Author(s):  
J. Fagir ◽  
A. Schubert ◽  
M. Frioud ◽  
D. Henke
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Single Channel ◽  
Research Area ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Surface Model ◽  
Digital Surface Model ◽  
Oblique View ◽  
Optical Images

The fusion of synthetic aperture radar (SAR) and optical data is a dynamic research area, but image segmentation is rarely treated. While a few studies use low-resolution nadir-view optical images, we approached the segmentation of SAR and optical images acquired from the same airborne platform – leading to an oblique view with high resolution and thus increased complexity. To overcome the geometric differences, we generated a digital surface model (DSM) from adjacent optical images and used it to project both the DSM and SAR data into the optical camera frame, followed by segmentation with each channel. The fused segmentation algorithm was found to out-perform the single-channel version.

scholarly journals High-resolution measurements of atmospheric molecular hydrogen and its isotopic composition at the West African coast of Mauritania

2012 ◽  
Vol 9 (12) ◽  
pp. 18799-18829
Author(s):  
S. Walter ◽  
A. Kock ◽  
T. Röckmann
Keyword(s):  
High Resolution ◽  
Isotopic Composition ◽  
Molecular Hydrogen ◽  
H2 Production ◽  
West African ◽  
Mixing Ratio ◽  
The West ◽  
Isotopic Signatures ◽  
West African Coast ◽  
African Coast

Abstract. Oceans are a net source of molecular hydrogen (N2) to the atmosphere, where nitrogen (N2) fixation is assumed to be the main biological production pathway besides photochemical production from organic material. The sources can be distinguished using isotope measurements because of clearly differing isotopic signatures of the produced hydrogen. Here we present the first ship-borne measurements of atmospheric molecular H2 mixing ratio and isotopic composition at the West African coast of Mauritania (16–25° W, 17–24° N). This area is one of the biologically most active regions of the world's oceans with seasonal upwelling events and characterized by strongly differing hydrographical/biological properties and phytoplankton community structures. The aim of this study was to identify areas of H2 production and distinguish H2 sources by isotopic signatures of atmospheric H2. Besides this a diurnal cycle of atmospheric H2 was investigated. For this more than 100 air samples were taken during two cruises in February 2007 and 2008, respectively. During both cruises a transect from the Cape Verde Island towards the Mauritanian Coast was sampled. In 2007 additionally four days were sampled with a high resolution of one sample per hour. Our results clearly indicate the influence of local sources and suggest the Banc d'Arguin as a pool for precursors for photochemical H2 production, whereas N2 fixation could not be identified as a H2 source during these two cruises. With our experimental setup we could demonstrate that variability in diurnal cycles is probably influenced and biased by released precursors for photochemical H2 production and the origin of air masses. This means for further investigations that just measuring the mixing ratio of H2 is insufficient to explain the variability of a diurnal cycle and support is needed, e.g. by isotopic measurements. However, measurements of H2 mixing ratios, which are easy to conduct online during ship cruises could be a helpful tool to easily identify production areas of biological precursors such as VOC's for further investigations.

THE LYMAN BANDS OF MOLECULAR HYDROGEN

1959 ◽  
Vol 37 (5) ◽  
pp. 636-659 ◽  
Author(s):  
G. Herzberg ◽  
L. L. Howe
Keyword(s):  
High Resolution ◽  
Vibrational Level ◽  
Ground State ◽  
Molecular Hydrogen ◽  
Equilibrium Constants ◽  
Dissociation Limit ◽  
Vibrational Levels ◽  
State Formula ◽  
Single Formula ◽  
Vibrational Constants

The Lyman bands of H2 have been investigated under high resolution with a view to improving the rotational and vibrational constants of H2 in its ground state. Precise Bv and ΔG values have been obtained for all vibrational levels of the ground state. One or two of the highest rotational levels of the last vibrational level (v = 14) lie above the dissociation limit. Both the [Formula: see text] and ΔG″ curves have a point of inflection at about v″ = 3. This makes it difficult to represent the whole course of each of these curves by a single formula and therefore makes the resulting equilibrium constants somewhat uncertain. This uncertainty is not very great for the rotational constants for which we find[Formula: see text]but is considerable for the vibrational constants ωe and ωexe for which three-, four-, five-, and six-term formulae give results diverging by ± 1 cm−1. The rotational and vibrational constants for the upper state [Formula: see text] of the Lyman bands are also determined. An appreciable correction to the position of the upper state is found.

scholarly journals DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1619 ◽  
Author(s):  
Zhou Ya’nan ◽  
Luo Jiancheng ◽  
Feng Li ◽  
Zhou Xiaocheng
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Hunan Province ◽  
Spatial Features ◽  
Optical Images ◽  
Multi Temporal ◽  
Crop Classification ◽  
Sar Data

Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification.

Classifications of High Resolution Optical Images using Supervised Algorithms

2018 ◽  
Author(s):  
Balnarsaiah Battula ◽  
Laxminarayana Parayitam ◽  
T. S. Prasad ◽  
Penta Balakrishna ◽  
Chandrasekhar Patibandla
Keyword(s):  
High Resolution ◽  
Optical Images

scholarly journals Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model

2018 ◽  
Vol 10 (9) ◽  
pp. 1459 ◽  
Author(s):  
Ying Sun ◽  
Xinchang Zhang ◽  
Xiaoyang Zhao ◽  
Qinchuan Xin
Keyword(s):  
High Resolution ◽  
Active Contour ◽  
Active Contour Model ◽  
Remote Sensing Data ◽  
Lidar Data ◽  
Boundary Extraction ◽  
Contour Model ◽  
Optical Images ◽  
Building Footprint ◽  
Salt And Pepper

Identifying and extracting building boundaries from remote sensing data has been one of the hot topics in photogrammetry for decades. The active contour model (ACM) is a robust segmentation method that has been widely used in building boundary extraction, but which often results in biased building boundary extraction due to tree and background mixtures. Although the classification methods can improve this efficiently by separating buildings from other objects, there are often ineluctable salt and pepper artifacts. In this paper, we combine the robust classification convolutional neural networks (CNN) and ACM to overcome the current limitations in algorithms for building boundary extraction. We conduct two types of experiments: the first integrates ACM into the CNN construction progress, whereas the second starts building footprint detection with a CNN and then uses ACM for post processing. Three level assessments conducted demonstrate that the proposed methods could efficiently extract building boundaries in five test scenes from two datasets. The achieved mean accuracies in terms of the F1 score for the first type (and the second type) of the experiment are 96.43 ± 3.34% (95.68 ± 3.22%), 88.60 ± 3.99% (89.06 ± 3.96%), and 91.62 ±1.61% (91.47 ± 2.58%) at the scene, object, and pixel levels, respectively. The combined CNN and ACM solutions were shown to be effective at extracting building boundaries from high-resolution optical images and LiDAR data.

High-Resolution Electron-Impact Study of the Far-Ultraviolet Emission Spectrum of Molecular Hydrogen

1995 ◽  
Vol 101 ◽  
pp. 375 ◽  
Author(s):  
Xianming Liu ◽  
Syed M. Ahmed ◽  
Rosalie A. Multari ◽  
Geoffrey K. James ◽  
Joseph M. Ajello
Keyword(s):  
High Resolution ◽  
Emission Spectrum ◽  
Electron Impact ◽  
Molecular Hydrogen ◽  
Impact Study ◽  
Ultraviolet Emission ◽  
Resolution Electron ◽  
Far Ultraviolet

APPLIED USE OF HIGH RESOLUTION IMAGERY FOR HUMAN SAFETY AND ENVIRONMENT IN SOUTH SUMATRA, INDONESIA

2005 ◽  
Vol 2005 (1) ◽  
pp. 819-823
Author(s):  
Sarah Terry ◽  
Khalid A. Soofi ◽  
Yuli Kwenandar ◽  
Bill Mcintosh
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Spectral Resolution ◽  
Oil And Gas ◽  
Synthetic Aperture ◽  
Vegetation Types ◽  
Response Planning ◽  
Optical Images ◽  
Human Habitat ◽  
Aperture Radar

ABSTRACT The availability of extremely high resolution images offers an unprecedented opportunity to use such images to monitor, maintain and ultimately preserve and rehabilitate the natural environment throughout the life cycle of oil and gas projects. The variety of images available range from optical images such as Landsat ETM1 imagery (14.25 meter/pixel), IKONOS2 imagery (1 meter/pixel) and QuickBird3 imagery (0.6 meter/pixel). These optical images have sufficient spatial and spectral resolution to detect different vegetation types (e.g. old growth vs. new plantations), cleared vegetation caused by logging or human habitat expansion, burned areas due to fire and vegetation stress caused by spills from oil pipelines or storage vessels. These images are also useful for identifying potential pollutant sources such as abandoned wells, old drilling pits or other remediation targets, as well as potential pollutant receptors. Areas which have perpetual cloud cover, such as South Sumatra, of Indonesia, can be monitored using Synthetic Aperture Radar (e.g. European Space Agency's Synthetic Aperture Radar and RadarSat International of Canada). Although a typical SAR does not have the spectral resolution of optical sensors, it does have the advantage of seeing through clouds. The radar backscatter is sensitive to surface roughness and Dielectric Constant which can be used quite effectively to discriminate major vegetation types. These images, when combined with normal GIS tools, take us beyond simple monitoring, to generating predictive tools for planning future sites for drilling wells and placement of facilities such as pipelines and roads. This paper will focus on the use of these techniques for oil spill response planning in South Sumatra, while taking note of other applications of remote sensing and GIS to oil and gas operations in the regional environment.

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