scholarly journals Formation of a fused image of the land surface based on pixel clustering of location images in a multi-position onboard system

2021 ◽  
Vol 20 (2) ◽  
pp. 302-340
Author(s):  
Vadim Nenashev ◽  
Igor Khanykov
Keyword(s):  
Land Surface ◽  
High Speed ◽  
Clustering Algorithm ◽  
Variable Number ◽  
Reference Points ◽  
Composite Image ◽  
Functional Transformation ◽  
Optical Images ◽  
Fused Image ◽  
Onboard System

The paper proposes a method for fusioning multi-angle images implementing the algorithm for quasi-optimal clustering of pixels to the original images of the land surface. The original multi-angle images formed by the onboard equipment of multi-positional location systems are docked into a single composite image and, using a high-speed algorithm for quasi-optimal pixel clustering, are reduced to several colors while maintaining characteristic boundaries. A feature of the algorithm of quasi-optimal pixel clustering is the generation of a series of partitions with gradually increasing detail due to a variable number of clusters. This feature allows you to choose an appropriate partition of a pair of docked images from the generated series. The search for reference points of the isolated contours is performed on a pair of images from the selected partition of the docked image. A functional transformation is determined for these points. And after it has been applied to the original images, the degree of correlation of the fused image is estimated. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the evaluation of the fusion quality is acceptable. The type of functional transformation is selected according to the images reduced in color, which later is applied to the original images. This process is repeated for clustered images with greater detail in the event that the assessment of the fusion quality is not acceptable. The purpose of present study is to develop a method that allows synthesizing fused image of the land surface from heteromorphic and heterogeneous images. The paper presents the following features of the fusing method. The first feature is the processing of a single composite image from a pair of docked source images by the pixel clustering algorithm, what makes it possible to isolate the same areas in its different parts in a similar way. The second feature consists in determining the functional transformation by the isolated reference points of the contour on the processed pair of clustered images, which is later applied to the original images to combine them. The paper presents the results on the synthesis of a fused image both from homogeneous (optical) images and from heterogeneous (radar and optical) images. A distinctive feature of the developed method is to improve the quality of synthesis, increase the accuracy and information content of the final fused image of the land surface.  

scholarly journals Formation of Fused Images of the Land Surface from Radar and Optical Images in Spatially Distributed On-Board Operational Monitoring Systems

2021 ◽  
Vol 7 (12) ◽  
pp. 251
Author(s):  
Vadim A. Nenashev ◽  
Igor G. Khanykov
Keyword(s):  
Quality Assessment ◽  
Information Content ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Reference Points ◽  
Operational Monitoring ◽  
Functional Transformation ◽  
Location System ◽  
Fused Image ◽  
Spatially Distributed

This paper considers the issues of image fusion in a spatially distributed small-size on-board location system for operational monitoring. The purpose of this research is to develop a new method for the formation of fused images of the land surface based on data obtained from optical and radar devices operated from two-position spatially distributed systems of small aircraft, including unmanned aerial vehicles. The advantages of the method for integrating information from radar and optical information-measuring systems are justified. The combined approach allows removing the limitations of each separate system. The practicality of choosing the integration of information from several widely used variants of heterogeneous sources is shown. An iterative approach is used in the method for combining multi-angle location images. This approach improves the quality of synthesis and increases the accuracy of integration, as well as improves the information content and reliability of the final fused image by using the pixel clustering algorithm, which produces many partitions into clusters. The search for reference points on isolated contours is carried out on a pair of left and right images of the docked image from the selected partition. For these reference points, a functional transformation is determined. Having applied it to the original multi-angle heterogeneous images, the degree of correlation of the fused image is assessed. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the quality assessment of the fusion becomes acceptable. The type of functional transformation is selected based on clustered images and then applied to the original multi-angle heterogeneous images. This process is repeated for clustered images with greater granularity in case if quality assessment of the fusion is considered to be poor. At each iteration, there is a search for pairs of points of the contour of the isolated areas. Areas are isolated with the use of two image segmentation methods. Experiments on the formation of fused images are presented. The result of the research is the proposed method for integrating information obtained from a two-position airborne small-sized radar system and an optical location system. The implemented method can improve the information content, quality, and reliability of the finally established fused image of the land surface.

scholarly journals Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image

2019 ◽  
Vol 11 (3) ◽  
pp. 327 ◽  
Author(s):  
Xia Wang ◽  
Feng Ling ◽  
Huaiying Yao ◽  
Yaolin Liu ◽  
Shuna Xu
Keyword(s):  
Surface Water ◽  
Real Time ◽  
Spatial Resolution ◽  
Water Body ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Body Mapping ◽  
Water Index ◽  
Land Surface Water ◽  
Body Map

Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image.

Characteristics and Predictability of Midwestern United States Drought

2021 ◽  
Author(s):  
Andrew Hoell ◽  
Trent W. Ford ◽  
Molly Woloszyn ◽  
Jason A. Otkin ◽  
Jon Eischeid
Keyword(s):  
United States ◽  
Great Plains ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Northern Great Plains ◽  
Ohio Valley ◽  
Midwestern United States ◽  
Wave Trains

AbstractCharacteristics and predictability of drought in the Midwestern United States, spanning the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916-2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and three-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for sub-annual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multi-annual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March-November in the NGP and all year in the OV, with a preference for March-May and September-November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is four times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons are related to atmospheric wave trains spanning the Pacific-North American sector, longer-lead predictability is limited to the OV in December-February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño-Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwestern drought.

Operation-oriented reliability and availability evaluation for onboard high-speed train control system with dynamic Bayesian network

2018 ◽  
Vol 233 (3) ◽  
pp. 455-469 ◽  
Author(s):  
Lei Jiang ◽  
Yiliu Liu ◽  
Xiaomin Wang ◽  
Mary Ann Lundteigen
Keyword(s):  
Control System ◽  
Bayesian Network ◽  
High Speed ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
High Speed Train ◽  
Train Control ◽  
Train Control System ◽  
Onboard System ◽  
Reliability And Availability

The reliability and availability of the onboard high-speed train control system are important to guarantee operational efficiency and railway safety. Failures occurring in the onboard system may result in serious accidents. In the analysis of the effects of failure, it is significant to consider the operation of an onboard system. This article presents a systemic approach to evaluate the reliability and availability for the onboard system based on dynamic Bayesian network, with taking into account dynamic failure behaviors, imperfect coverage factors, and temporal effects in the operational phase. The case studies are presented and compared for onboard systems with different redundant strategies, that is, the triple modular redundancy, hot spare double dual, and cold spare double dual. Dynamic fault trees of the three kinds of onboard system are constructed and mapped into dynamic Bayesian networks. The forward and backward inferences are conducted not only to evaluate the reliability and availability but also to recognize the vulnerabilities of the onboard systems. A sensitivity analysis is carried out for evaluating the effects of failure rates subject to uncertainties. To improve the reliability and availability, the recovery mechanism should be paid more attention. Finally, the proposed approach is validated with the field data from one railway bureau in China and some industrial impacts are provided.

scholarly journals Blade Tip-timing Technology with Multiple Reference Phases for Online Monitoring of High-speed Blades under Variable-speed Operation

2018 ◽  
Vol 18 (6) ◽  
pp. 243-250 ◽  
Author(s):  
Zhang Ji-wang ◽  
Zhang Lai-bin ◽  
Ding Ke-Qin ◽  
Duan Li-xiang
Keyword(s):  
High Speed ◽  
Online Monitoring ◽  
Rotation Speed ◽  
Experimental Tests ◽  
Reference Points ◽  
Variable Speed ◽  
Rotating Shaft ◽  
Virtual Reference ◽  
Blade Tip ◽  
Multiple Reference

Abstract High-speed blades form core mechanical components in turbomachines. Research concerning online monitoring of operating states of such blades has drawn increased attention in recent years. To this end, various methods have been devised, of which, the blade tip-timing (BTT) technique is considered the most promising. However, the traditional BTT method is only suitable for constant-speed operations. But in practice, the rotational speed of turbomachine blades is constantly changing under the influence of external factors, which lead to unacceptable errors in measurement. To tackle this problem, a new BTT method based on multi-phases is proposed. A plurality of phases was arranged as evenly as possible on the rotating shaft to determine the rotation speed. Meanwhile, the corresponding virtual reference point was determined in accordance with the number of blades between consecutive phases. Based on these reference points, equations to measure displacement due to blade vibrations were deduced. Finally, mathematical modeling, numerical simulation and experimental tests were performed to verify the validity of the proposed method. Results demonstrate that the error in measurement induced when using the proposed method is less than 1.8 %, which is much lower compared to traditional methods utilized under variable-speed operation.

scholarly journals Technology and human purpose: the problem of solids transport on the earth's surface

2012 ◽  
Vol 3 (1) ◽  
pp. 417-431 ◽  
Author(s):  
P. K. Haff
Keyword(s):  
Land Surface ◽  
High Speed ◽  
Large Scale ◽  
Potential Gradient ◽  
High Energy ◽  
Transport Infrastructure ◽  
Advective Transport ◽  
Long Distance ◽  
Solids Transport ◽  
Human Purpose

Abstract. Displacement of mass of limited deformability ("solids") on the Earth's surface is opposed by friction and (the analog of) form resistance – impediments relaxed by rotational motion, self-powering of mass units, and transport infrastructure. These features of solids transport first evolved in the biosphere prior to the emergence of technology, allowing slope-independent, diffusion-like motion of discrete objects as massive as several tons, as illustrated by animal foraging and movement along game trails. However, high-energy-consumption technology powered by fossil fuels required a mechanism that could support advective transport of solids, i.e., long-distance, high-volume, high-speed, unidirectional, slope independent transport across the land surface of materials like coal, containerized fluids, and minerals. Pre-technology nature was able to sustain large-scale, long-distance solids advection only in the limited form of piggybacking on geophysical flows of water (river sediment) and air (dust). The appearance of a generalized mechanism for advection of solids independent of fluid flows and gravity appeared only upon the emergence of human purpose. Purpose enables solids advection by, in effect, enabling a simulated continuous potential gradient, otherwise lacking, between discrete and widely separated fossil-fuel energy sources and sinks. Invoking purpose as a mechanism in solids advection is an example of the need to import anthropic principles and concepts into the language and methodology of modern Earth system dynamics. As part of the emergence of a generalized solids advection mechanism, several additional transport requirements necessary to the function of modern large-scale technological systems were also satisfied. These include spatially accurate delivery of advected payload, targetability to essentially arbitrarily located destinations (such as cities), and independence of structure of advected payload from transport mechanism. The latter property enables the transport of an onboard power supply and delivery of persistent-memory, high-information-content payload, such as technological artifacts ("parts").

Formation of Methane Hydrates from Super-compressed Water and Methane Mixtures

2010 ◽  
Vol 1262 ◽  
Author(s):  
Jing-Yin Chen ◽  
Choong-Shik Yoo
Keyword(s):  
Methane Hydrate ◽  
High Speed ◽  
Energy Resource ◽  
Hydrate Formation ◽  
Methane Hydrates ◽  
Stability Field ◽  
Compression Rate ◽  
Time Resolved ◽  
Optical Images ◽  
The Stability

AbstractUnderstanding the high-pressure kinetics associated with the formation of methane hydrates is critical to the practical use of the most abundant energy resource on earth. In this study, we have studied, for the first time, the compression rate dependence on the formation of methane hydrates under pressures, using dynamic-Diamond Anvil Cell (d-DAC) coupled with a high-speed microphotography and a confocal micro-Raman spectroscopy. The time-resolved optical images and Raman spectra indicate that the pressure-induced formation of methane hydrate depends on the compression rate and the peak pressure. At the compression rate of around 5 to 10 GPa/s, methane hydrate phase II (MH-II) forms from super-compressed water within the stability field of ice VI between 0.9 GPa and 2.0 GPa. This is due to a relatively slow rate of the hydrate formation below 0.9 GPa and a relatively fast rate of the water solidification above 2.0 GPa. The fact that methane hydrate forms from super-compressed water underscores a diffusion-controlled growth, which accelerates with pressure because of the enhanced miscibility between methane and super-compressed water.

scholarly journals On Using a Clustering Approach for Global Climate Classification

2016 ◽  
Vol 29 (9) ◽  
pp. 3387-3401 ◽  
Author(s):  
Pawel Netzel ◽  
Tomasz Stepinski
Keyword(s):  
Land Surface ◽  
Clustering Algorithm ◽  
Global Climate ◽  
Multivariate Time Series ◽  
Clustering Algorithms ◽  
Skewed Distribution ◽  
Climatic Variables ◽  
Climate Classification ◽  
Local Climate ◽  
Partitioning Around Medoids

Abstract Classifying the land surface into climate types provides means of diagnosing relations between Earth’s physical and biological systems and the climate. Global climate classifications are also used to visualize climate change. Clustering climate datasets provides a natural approach to climate classification, but the rule-based Köppen–Geiger classification (KGC) is the one most widely used. Here, a comprehensive approach to the clustering-based classification of climates is presented. Local climate is defined as a multivariate time series of mean monthly climatic variables and the authors propose to use dynamic time warping (DTW) as a measure of dissimilarity between local climates. Also discussed are the choice of climatic variables, the importance of their proper normalization, and the advantage of using distance-based clustering algorithms. Using the WorldClim global climate dataset and different combinations of clustering parameters, 32 different clustering-based classifications are calculated. These classifications are compared between themselves and to the KGC using the information-theoretic V measure. It is found that the best classifications are obtained using three climate variables (temperature, precipitation, and temperature range), a data normalization that takes into account the skewed distribution of precipitation values, and the partitioning around medoids clustering algorithm. Two such classifications are compared in detail between each other and to the KGC. About half of the climate types found by clustering can be matched to the familiar KGC classes, but the rest differ in their climatic character and spatial distribution. Finally, it is demonstrated that clustering-based classification results in climate types that are internally more homogeneous and externally more distinct than climate types in the KGC.

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