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检索条件"任意字段=Conference on High-Performance Computing in Geoscience and Remote Sensing VI"

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Generation of OAM waves using metamaterials substrate antenna 6

Generation of OAM waves using metamaterials substrate antenn...

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conference on high-performance computing in geoscience and remote sensing vi

作者： Meng, X. S. Wu, J. J. Wu, Z. S. Qu, T. Xidian Univ Sch Elect Engn Xian 710071 Shaanxi Peoples R China Xidian Univ Sch Phys & Optoelect Engn Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781510604186;9781510604193

In this paper, we demonstrate a single layer X-band (8-12 GHz) metamaterials substrate antenna for generating orbital angular momentum (OAM) beam. The proposed design consists of a series of phase-shift unit cells to control the emergent phase, whose function is same as spiral phase plate (SPP). The SPP uses the concept of reflectarray antenna having a double circular ring type unit cell to control the reflection phase for generating beams carrying OAM wave. The results of phase patterns simulation verify that the vortex radio waves can be generated by using sub-wavelength reflective metamaterials substrate antenna. From the perspective of the electromagnetic calculation, we can use high performance computing to reduce the computation time

关键词： OAM waves SPP reflectarray antenna vortex radio waves metamaterials high performance computing

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Parallel Hyperspectral Image Reconstruction Using Random Projections 6

Parallel Hyperspectral Image Reconstruction Using Random Pro...

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conference on high-performance computing in geoscience and remote sensing vi

作者： Sevilla, Jorge Martin, Gabriel Nascimento, Jose M. P. Inst Telecomunicacoes Lisbon Portugal Lab Instrumentat & Expt Particle Phys Lisbon Portugal Inst Super Engn Lisboa Lisbon Portugal

ISBN: (纸本)9781510604186;9781510604193

Spaceborne sensors systems are characterized by scarce onboard computing and storage resources and by communication links with reduced bandwidth. Random projections techniques have been demonstrated as an effective and very light way to reduce the number of measurements in hyperspectral data, thus, the data to be transmitted to the Earth station is reduced. However, the reconstruction of the original data from the random projections may be computationally expensive. SpeCA is a blind hyperspectral reconstruction technique that exploits the fact that hyperspectral vectors often belong to a low dimensional subspace. SpeCA has shown promising results in the task of recovering hyperspectral data from a reduced number of random measurements. In this manuscript we focus on the implementation of the SpeCA algorithm for graphics processing units (GPU) using the compute unified device architecture (CUDA). Experimental results conducted using synthetic and real hyperspectral datasets on the GPU architecture by NviDIA: GeForce GTX 980, reveal that the use of GPUs can provide real-time reconstruction. The achieved speedup is up to 22 times when compared with the processing time of SpeCA running on one core of the Intel i7-4790K CPU (3.4GHz), with 32 Gbyte memory.

关键词： Hyperspectral Compressive sensing Hyperspectral Random Projections high performance computing Graphics Processing Units (GPU)

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OpenCL-library-based Implementation of SCLSU Algorithm for remotely Sensed Hyperspectral Data Exploitation: clMAGMA versus viennaCL 6

OpenCL-library-based Implementation of SCLSU Algorithm for R...

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conference on high-performance computing in geoscience and remote sensing vi

作者： Bernabe, Sergio Botella, Guillermo Orueta, Carlos Navarro, Jose M. R. Prieto-Matias, Manuel Plaza, Antonio Univ Complutense Madrid Dept Comp Architecture & Automat E-28040 Madrid Spain Univ Extremadura Hyperspectral Comp Lab E-10003 Caceres Spain

ISBN: (纸本)9781510604186;9781510604193

In the last decade, hyperspectral spectral unmixing (HSU) analysis have been applied in many remote sensing applications. For this process, the linear mixture model (LMM) has been the most popular tool used to find pure spectral constituents or endmembers and their fractional abundance in each pixel of the data set. The unmixing process consists of three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fractional abundance of each endmember in each pixel of the scene. However, unmixing algorithms can be very expensive computationally, a fact that compromises their use in applications under real-time constraints. This is, mainly, due to the last two stages in the unmixing process, which are the most consuming ones. In this work, we propose parallel opencl-library-based implementations of the sum-to-one constrained least squares unmixing (P-SCLSU) algorithm to estimate the per-pixel fractional abundances by using mathematical libraries such as clMAGMA or viennaCL. To the best of our knowledge, this kind of analysis using OpenCL libraries have not been previously conducted in the hyperspectral imaging processing literature, and in our opinion it is very important in order to achieve efficient implementations using parallel routines. The efficacy of our proposed implementations is demonstrated through Monte Carlo simulations for real data experiments and using high performance computing (HPC) platforms such as commodity graphics processing units (GPUs).

关键词： Hyperspectral imaging spectral unmixing high performance computing (HPC) OpenCL clMAGMA viennaCL

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A Multiple Criteria-based Spectral Partitioning Method for remotely Sensed Hyperspectral Image Classification 6

A Multiple Criteria-based Spectral Partitioning Method for R...

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conference on high-performance computing in geoscience and remote sensing vi

作者： Liu, Yi Li, Jun Plaza, Antonio Sun, Yanli Univ Extremadura Escuela Politecn Dept Technol Comp & Commun Hyperspectral Comp Lab Hypercomp E-10071 Caceres Spain Sun Yat Sen Univ Sch Geog & Planning Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Guangdong Key Lab Urbanizat & Geosimulat Guangzhou Guangdong Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China

ISBN: (纸本)9781510604186;9781510604193

Hyperspectral remote sensing offers a powerful tool in many different application contexts. The imbalance between the high dimensionality of the data and the limited availability of training samples calls for the need to perform dimensionality reduction in practice. Among traditional dimensionality reduction techniques, feature extraction is one of the most widely used approaches due to its flexibility to transform the original spectral information into a subspace. In turn, band selection is important when the application requires preserving the original spectral information (especially the physically meaningful information) for the interpretation of the hyperspectral scene. In the case of hyperspectral image classification, both techniques need to discard most of the original features/bands in order to perform the classification using a feature set with much lower dimensionality. However, the discriminative information that allows a classifier to provide good performance is usually class dependent and the relevant information may live in weak features/bands that are usually discarded or lost through subspace transformation or band selection. As a result, in practice, it is challenging to use either feature extraction or band selection for classification purposes. Relevant lines of attack to address this problem have focused on multiple feature selection aiming at a suitable fusion of diverse features in order to provide relevant information to the classifier. In this paper, we present a new dimensionality reduction technique, called multiple criteria-based spectral partitioning, which is embedded in an ensemble learning framework to perform advanced hyperspectral image classification. Driven by the use of a multiple band priority criteria that is derived from classic band selection techniques, we obtain multiple spectral partitions from the original hyperspectral data that correspond to several band subgroups with much lower spectral dimensionality as compared with

关键词： Hyperspectral remote sensing image classification spectral partitioning multiple band selecting criteria high-performance computing

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A New Semi-Supervised Classification Strategy Combining Active Learning and Spectral Unmixing of Hyperspectral Data 6

A New Semi-Supervised Classification Strategy Combining Acti...

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conference on high-performance computing in geoscience and remote sensing vi

作者： Sun, Yanli Zhang, Xia Plaza, Antonio Li, Jun Dopido, Inmaculada Liu, Yi Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Univ Extremadura Dept Technol Comp & Commun Hyperspectral Comp Lab Hypercomp E-10003 Caceres Spain Sun Yat Sen Univ Sch Geog & Planning Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Guangdong Key Lab Urbanizat & Geosimulat Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781510604186;9781510604193

Hyperspectral remote sensing allows for the detailed analysis of the surface of the Earth by providing high-dimensional images with hundreds of spectral bands. Hyperspectral image classification plays a significant role in hyperspectral image analysis and has been a very active research area in the last few years. In the context of hyperspectral image classification, supervised techniques (which have achieved wide acceptance) must address a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. Semi-supervised learning offers an effective solution that can take advantage of both unlabeled and a small amount of labeled samples. Spectral unmixing is another widely used technique in hyperspectral image analysis, developed to retrieve pure spectral components and determine their abundance fractions in mixed pixels. In this work, we propose a method to perform semi-supervised hyperspectral image classification by combining the information retrieved with spectral unmixing and classification. Two kinds of samples that are highly mixed in nature are automatically selected, aiming at finding the most informative unlabeled samples. One kind is given by the samples minimizing the distance between the first two most probable classes by calculating the difference between the two highest abundances. Another kind is given by the samples minimizing the distance between the most probable class and the least probable class, obtained by calculating the difference between the highest and lowest abundances. The effectiveness of the proposed method is evaluated using a real hyperspectral data set collected by the airborne visible infrared imaging spectrometer (AviRIS) over the Indian Pines region in Northwestern Indiana. In the pa

关键词： Hyperspectral remote sensing image classification semi-supervised learning spectral unmixing

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GPU Implementation of Discrete Particle Swarm Optimization Algorithm for Endmember Extraction From Hyperspectral Image 7

GPU Implementation of Discrete Particle Swarm Optimization A...

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conference on high-performance computing in geoscience and remote sensing viI

作者： Yu, Chaoyin Yuan, Zhengwu Wu, Yuanfeng Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Key Lab Digital Earth Sci Beijing 100094 Peoples R China

ISBN: (纸本)9781510613256;9781510613249

Hyperspectral image unmixing is an important part of hyperspectral data analysis. The mixed pixel decomposition consists of two steps, endmember (the unique signatures of pure ground components) extraction and abundance (the proportion of each endmember in each pixel) estimation. Recently, a Discrete Particle Swarm Optimization algorithm (DPSO) was proposed for accurately extract endmembers with high optimal performance. However, the DPSO algorithm shows very high computational complexity, which makes the endmember extraction procedure very time consuming for hyperspectral image unmixing. Thus, in this paper, the DPSO endmember extraction algorithm was parallelized, implemented on the CUDA (GPU K20) platform, and evaluated by real hyperspectral remote sensing data. The experimental results show that with increasing the number of particles the parallelized version obtained much higher computing efficiency while maintain the same endmember exaction accuracy.

关键词： Hyperspectral image Endmember extraction Discrete particle swarm optimization (DPSO) Ggraphicss processing units (GPUs) high-performance computing

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EVALUATING MODIS VEGETATION INDICES USING GROUND BASED MEASUREMENTS IN MOUNTAIN SEMI-NATURAL MEADOWS OF NORTHEAST PORTUGAL 30

EVALUATING MODIS VEGETATION INDICES USING GROUND BASED MEASU...

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30th IEEE International geoscience and remote sensing Symposium (IGARSS) on remote sensing - Global vision for Local Action

作者： Cunha, Mario Pocas, Isabel Marcal, Andre R. S. Rodrigues, Arlete Pereira, Luis S. Univ Porto Fac Ciencias Oporto Portugal FCUP Ctr Investigacao Ciencias Geoespaciais Porto Portugal Tech Univ Ctr Engn Biossistemas CEER ISA UTL Lisbon Portugal

ISBN: (纸本)9781424495665

The sustainable conservation of mountain semi-natural meadows depends on the knowledge of their vegetation dynamics and management practices. Time series of vegetation indices (vi) derived from high temporal resolution satellite images can be a useful tool to the sustainable management of semi-natural meadows ecosystem and grazing activities. In this study satellite vi from the Moderate Resolution Imaging Spectroradiometer (MODIS) are evaluated against in situ measurements of vis and plant height in the semi-natural mountain meadows of Northeast Portugal. In two testes sites, we evaluated the performance of Normalized Difference Vegetation Index (NDvi) and Enhanced Vegetation Index (Evi) from MODIS and field spectroradiometer sensor in characterizing semi-natural meadows phenology and plant height. The Savitzky-Golay filter was used for smoothing each vi time series, as well as to extract a number of NDvi and Evi metrics by computing derivatives. There was weak to reasonable agreement between vis-metrics from MODIS and ground based derived phenology. The NDvi had a great sensitivity to crop growth changes during start of growth season, whereas the Evi exhibited more sensitivity at the pick of the maximum green biomass. The relationship between vegetation height and both vi from MODIS or field spectroradiometer, fit a non-linear model with similar pattern function for each test site. Regression analysis revealed that 67% of the in-season plant height variability could be explained by MODISEvi. These results suggest a great sensibility of MODISEvi to detect the phenology and plant height of semi-natural meadows, even in situations of high plant height.

关键词： Vegetation Agriculture remote sensing

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Polarization remote sensing of atmospheric coated-spherical aerosol based on optical vortex and parallel acceleration 8

Polarization remote sensing of atmospheric coated-spherical ...

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conference on high-performance computing in geoscience and remote sensing viII

作者： Guo, Lixin Shi, Chen-ge Huang, Qingqing Cheng, Mingjian Li, Jiangting Xidian Univ Sch Phys & Optoelect Engn Xian 710071 Shaanxi Peoples R China State Key Lab Pulsed Power Laser Technol Hefei 230037 Anhui Peoples R China

ISBN: (数字)9781510621688

ISBN: (纸本)9781510621688

Based on the scattering effect of vortex beams, the remote sensing for aerosol particles in different atmosphere environments is investigated. In this paper we regard the aerosol particle to a coated-spherical particle other than an uniform single particle as it's more consistent with the way it exists in the actual atmosphere. The influences of orbital angular momentum (OAM) mode, the beam width of vortex beams, the outer and inner radius on the performance on the remote sensing for coated-spherical particles in marine atmosphere are analyzed numerically. Results indicated that the runtime will sharply increase when the number of the orbital angular momentum modes is large enough. Therefore, the parallel acceleration technology is used by us in this paper to solve this problem.

关键词： topological charge coated-spherical vortex beam aerosol particles Mie scattering

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high performance artificial SAR raw data generation algorithms for remote-sensed imaging applications

High performance artificial SAR raw data generation algorith...

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IEEE International geoscience and remote sensing Symposium (IGARSS 2002)/24th Canadian Symposium on remote sensing

作者： Rodríguez, D Rueda, D Nava, H Quinchanegua, A Univ Puerto Rico Dept Elect & Comp Engn Mayaguez PR 00681 USA

ISBN: (纸本)078037536X

This on going seminal work presents a new methodology for the design and implementation of high performance artificial raw data generation algorithms for remote-sensed imaging applications. Special attention is given in this work to synthetic aperture radar (SAR) system modeling and simulation imaging applications in the geosciences. Of particular importance are processes such as soil moisture content, backscattering from crops, nearshore ocean surface currents, and subsurface imaging in hyperarid regions. Our computing approach is based on the successful use of cross-ambiguity functions, in a Weyl-Heisenberg computational framework, as surface point target response functions for nonlinearly modulated, time-frequency structured, artificially created transmitted signals for our SAR system raw data modeling and simulation efforts. The functions are correlated with prescribed target reflectivity density functions to produce the desired results.

关键词： Radar imaging

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Coastline Change Mapping using a Spectral Band Method and Sobel Edge Operator 6

Coastline Change Mapping using a Spectral Band Method and So...

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conference on high-performance computing in geoscience and remote sensing vi

作者： Al-Mansoori, Saeed Al Marzouqi, Fatima MBRSC Dubai U Arab Emirates

ISBN: (纸本)9781510604186;9781510604193

Coastline extraction has become an essential activity in wake of the natural disasters taking place in some regions such as tsunami, flooding etc. Salient feature of such catastrophes is lack of reaction time available for combating emergency, thus it is the endeavor of any country to develop constant monitoring mechanism of shorelines. This is a challenging task because of the magnitude of changes taking place to the coastline regularly. Previous research findings highlight a need of formulating automation driven methodology for timely and accurate detection of alterations in the coastline impacting sustainability of mankind operating in the coastal zone. In this study, we propose a new approach for automatic extraction of the coastline using remote sensing data. This approach is composed of three main stages. Firstly, classifying pixels of the image into two categories i.e. land and water body by applying two normalized difference indices i.e. Normalized Difference Vegetation Index (NDvi) and Normalized Difference Water Index (NDWI). Then, the process of binary conversion of classified image takes place using a local threshold method. Finally, the coastline is extracted by applying Sobel edge operator with a pair of (3x3) kernels. The approach is tested using 2.5m DubaiSat-1 (DS1) and DubaiSat-2 (DS2) images captured to detect and monitor the changes occurring along Dubai coastal zone within a period of six years from 2009 till 2015. Experimental results prove that the approach is capable of extracting the coastlines from DS1 and DS2 images with moderate human interaction. The results of the study show an increase of 6% in Dubai shoreline resulting on account of numerous man-made infrastructure development projects in tourism and allied sectors.

关键词： Coastline extraction remote sensing Band ratios Normalized Difference Vegetation Index (NDvi) Normalized Difference Water Index (DNWI) DubaiSat-1 DubaiSat-2 Sobel edge operator

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