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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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performance of Global 3D model retrievals of the Martian surface using the UCL CASP-GO system on CTX stereo images on Linux clusters and Microsoft Azure® cloud computing platforms 8

Performance of Global 3D model retrievals of the Martian sur...

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

作者： Tao, Y. Muller, J-P. Univ Coll London Imaging Grp Mullard Space Sci Lab Dorking RH5 6NT Surrey England

ISBN: (数字)9781510621688

ISBN: (纸本)9781510621688

In this paper we introduce the Mars planet-wide 3D surface modelling work performed within the EU FP-7 iMars project which completed last year. In this report, we describe a fully automated multi-resolution DTM processing chain developed by the Imaging Group at UCL-MSSL, called CASP-GO based upon the heritage NASA Ames Stereo Pipeline (ASP) and the Gotcha image matcher. The CASP-GO system has been integrated into the Microsoft Azure cloud computing environment and successfully processed similar to 5,300 unique CTX DTMs covering similar to 19% of the Martian surface at 18m resolution.

关键词： 3D model DTM CASP-GO cloud computing Mars stereo CTX

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Intelligent Interpretation Technology of remote sensing Images based on Deep Learning Algorithms

Intelligent Interpretation Technology of Remote Sensing Imag...

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2024 International conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sui, Xuelian China Resource Satellite Application Center Beijing China

ISBN: (纸本)9798350360660

In recent years, high-resolution remote sensing images have been increasingly used in the field of geoscience. Therefore, how to interpret high-resolution remote sensing images with high quality is an urgent problem to be solved. This paper takes high-resolution remote sensing images as the research object, uses deep convolutional neural network (CNN) technology, conducts remote sensing image segmentation and feature extraction based on deep convolutional neural network, establishes a high-precision remote sensing image interpretation model, and improves the accuracy and computing speed of intelligent recognition of remote sensing images. This paper uses residual network (ResNet) as a learning sample to compare its performance with the classical algorithm on the same set of samples. Preliminary studies of this project have shown that the accuracy of convolutional neural networks in image analysis can reach 97.1%, and it shows obvious superiority at multiple spatial scales, and can effectively improve the efficiency of image interpretation. © 2024 IEEE.

关键词： Convolutional neural networks

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Research and implementation on spatial data storage and operation based on Hadoop platform

Research and implementation on spatial data storage and oper...

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2010 2nd IITA conference on geoscience and remote sensing, IITA-GRS 2010

作者： Wang, Yonggang Wang, Sheng Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing China Beijing Easymap Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9781424485154

As the deep development of spatial information sharing service, it brings forward high request to usability and expansibility of supporting system. Based on large-scale scalable server cluster, cloud computing brings hopes to resolves the existing difficult problems in the domain of geospatial information service. In this paper, we imported cloud computing technology including MapReduce model and Hadoop platform into the domain of geographic information system (GIS). Those key technology problems in the application of GIS such as spatial data storage, spatial index and spatial operation were described and studied in detail. We evaluated the performance and efficiency of spatial operation in Hadoop experiment environment with the real world data set. It demonstrates the applicability of cloud computing technology in computing-intensive spatial applications. © 2010 IEEE.

关键词： MapReduce

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Multiclass Change Detection for Multidimensional Images in the Presence of Noise 8

Multiclass Change Detection for Multidimensional Images in t...

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

作者： Lopez-Fandino, Javier Heras, Dora B. Arguello, Francisco Univ Santiago de Compostela Ctr Singular Invest Tecnoloxias Informac CiTIUS Santiago De Compostela Spain Univ Santiago de Compostela Dept Elect & Computac Santiago De Compostela Spain

ISBN: (数字)9781510621688

ISBN: (纸本)9781510621688

Change Detection (CD) techniques applied over multitemporal multispectral or hyperspectral remote sensing images allow monitoring changes in the land use or catastrophe tracking, among other applications. A multiclass CD technique for multidimensional images that is robust in the presence of noise is presented in this paper. The technique combines fusion at feature level to perform a first change/no change labeling (binary CD) and a later stage with fusion at decision level that performs a supervised multidate classification of the changed pixels (multiclass CD) obtaining the final from-to change map. The acquisition of multidimensional images usually corrupts the original signal by adding noise. This noise can be related with natural random processes or it can be produced during the sensor operation. Additive White Gaussian Noise (AWGN) and speckle noise simulate these effects. In this paper the robustness of the proposed CD technique in noisy scenarios for these two types of noise of varying intensity is evaluated. The experimental results show that the proposed technique is more robust than other alternatives, achieving accuracies close to those obtained in the absence of noise. The proposed technique is designed to be efficiently computed in GPU, thus dealing with the high computational cost of the processing of multidimensional images.

关键词： Multiclass change detection remote sensing noise classification GPU

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Parallel positive Boolean function approach to classification of remote sensing images

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Journal of Applied remote sensing 2011年第1期5卷

作者： Chang, Yang-Lang Hsieh, Tung-Ju Plaza, Antonio Chen, Yen-Lin Liang, Wen-Yew Fang, Jyh-Perng Huang, Bormin National Taipei University of Technology Department of Electrical Engineering No. 1 Chung-Hsiao E. Road Taipei 10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering No. 1 Chung-Hsiao E. Road Taipei 10608 Taiwan University of Extremadura Department of Technology of Computers and Communications Escuela Politecnica 10003 Caceres Spain University of Wisconsin-Madison Cooperative Institute for Meteorological Satellite Studies Space Science and Engineering Center Madison WI 53706 United States

We present a parallel image classification approach, referred to as the parallel positive Boolean function (PPBF), to multisource remote sensing images. PPBF is originally from the positive Boolean function (PBF) classifier scheme. The PBF multiclassifier is developed from a stack filter to classify specific classes of land covers. In order to enhance the efficiency of PBF, we propose PPBF to reduce the execution time using parallel computing techniques. PPBF fully utilizes the significant parallelism embedded in PBF to create a set of PBF stack filters on each parallel node based on different classes of land uses. It is implemented by combining the message-passing interface library and the open multiprocessing (OpenMP) application programing interface in a hybrid mode. The experimental results demonstrate that PPBF significantly reduces the computational loads of PBF classification. © 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Message passing

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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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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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IGARSS 2021 Virtual Symposium: Impressions of the First Days [conference Reports]

IGARSS 2021 Virtual Symposium: Impressions of the First Days...

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作者： Moreira, Alberto Judge, Jasmeet Bovolo, Francesca Plaza, Antonio

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