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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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Wavelet-based Multi-component Denoising on GPU to Improve the Classification of Hyperspectral Images 7

Wavelet-based Multi-component Denoising on GPU to Improve th...

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

作者： Quesada-Barriuso, Pablo Heras, Dora B. Arguello, Francisco Mourino, J. C. Ctr Singular Invest Tecnoloxias Informac CiTIUS Santiago De Compostela Spain Univ Santiago de Compostela Dept Elect & Comp Santiago De Compostela Spain Fdn Publ Galega Ctr Tecnol Supercomp Galicia CESGA Galicia Spain

ISBN: (纸本)9781510613256;9781510613249

Supervised classification allows handling a wide range of remote sensing hyperspectral applications. Enhancing the spatial organization of the pixels over the image has proven to be beneficial for the interpretation of the image content, thus increasing the classification accuracy. Denoising in the spatial domain of the image has been shown as a technique that enhances the structures in the image. This paper proposes a multi-component denoising approach in order to increase the classification accuracy when a classification method is applied. It is computed on multicore CPUs and NviDIA GPUs. The method combines feature extraction based on a 1D discrete wavelet transform (DWT) applied in the spectral dimension followed by an Extended Morphological Profile (EMP) and a classifier (SVM or ELM). The multi-component noise reduction is applied to the EMP just before the classification. The denoising recursively applies a separable 2D DWT after which the number of wavelet coefficients is reduced by using a threshold. Finally, inverse 2D-DWT filters are applied to reconstruct the noise free original component. The computational cost of the classifiers as well as the cost of the whole classification chain is high but it is reduced achieving real-time behavior for some applications through their computation on NviDIA multi-GPU platforms.

关键词： Land cover classification Hyperspectral analysis Wavelet transform Denoising Spectral-spatial processing high-performance computing Multi-thread Multi-GPU

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Hyperspectral Compressive sensing - A Low Power Consumption Approach 8

Hyperspectral Compressive Sensing - A Low Power Consumption ...

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

作者： Nascimento, Jose M. P. V'estias, Mario Duarte, Rui Inst Telecomunicacoes Lisbon Portugal IPL Inst Super Engn Lisboa Lisbon Portugal INESC ID Lisbon Portugal Univ Lisbon Inst Super Tecn Lisbon Portugal

ISBN: (数字)9781510621688

ISBN: (纸本)9781510621688

Hyperspectral imaging instruments allow data collection in hundreds of spectral bands for the same area on the surface of the Earth. The resulting multidimensional data cube typically comprises several GBs per light. Due to the extremely large volumes of data collected by imaging spectrometers, hyperspectral data compression, dimensionality reduction and Compressive sensing (CS) techniques has received considerable interest in recent years. These data are usually acquired by a satellite or an airbone instrument and sent to a ground station on Earth for subsequent processing. Usually the bandwidth connection between the satellite/airborne platform and the ground station is reduced, which limits the amount of data that can be transmitted. As a result, there is a clear need for (either lossless or lossy) hyperspectral data compression techniques that can be applied on-board the imaging instrument. This paper, presents a study of the power and time consumption and accuracy of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform, which is well suited to perform vector operations such as dot products. This implementation exploits the architecture at low level, using shared memory and coalesced accesses to memory. The conducted experiments have been performed to demonstrate the applicability, in terms of accuracy, time consuming and power consumption of these methods for onboard processing. The results show that by using this low power consumption GPU is it possible to obtain real-time performance with a very limited power requirement.

关键词： Hyperspectral imaging Compressed sensing Data modeling Image compression Reconstruction algorithms Satellites Sensors

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Parallel K-Dimensional Tree Classification Based on Semi-Matroid Structure for remote sensing Applications

Parallel K-Dimensional Tree Classification Based on Semi-Mat...

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conference on Satellite Data Compression, Communications, and Processing vi

作者： Chang, Yang-Lang Chen, Zhi-Ming Liu, Jin-Nan Chang, Lena Fang, Jyh Perng Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan Natl Taiwan Ocean Univ Dept Commun & Guidance Engn Taipei Taiwan

ISBN: (纸本)9780819483065

Satellite remote sensing images can be interpreted to provide important information of large-scale natural resources, such as lands, oceans, mountains, rivers, forests and minerals for Earth observations. Recent advances of remote sensing technologies have improved the availability of satellite imagery in a wide range of applications including high dimensional remote sensing data sets (e. g. high spectral and high spatial resolution images). The information of high dimensional remote sensing images obtained by state-of-the-art sensor technologies can be identified more accurately than images acquired by conventional remote sensing techniques. However, due to its large volume of image data, it requires a huge amount of storages and computing time. In response, the computational complexity of data processing for high dimensional remote sensing data analysis will increase. Consequently, this paper proposes a novel classification algorithm based on semi-matroid structure, known as the parallel k-dimensional tree semi-matroid (PKTSM) classification, which adopts a new hybrid parallel approach to deal with high dimensional data sets. It is implemented by combining the message passing interface (MPI) library, the open multi-processing (OpenMP) application programming interface and the compute unified device architecture (CUDA) of graphics processing units (GPU) in a hybrid mode. The effectiveness of the proposed PKTSM is evaluated by using MODIS/ASTER airborne simulator (MASTER) images and airborne synthetic aperture radar (AIRSAR) images for land cover classification during the Pacrim II campaign. The experimental results demonstrated that the proposed hybrid PKTSM can significantly improve the performance in terms of both computational speed-up and classification accuracy.

关键词： K-dimensional tree Semi-matroid structure high dimensional remote sensing images Graphics processing units (GPU)

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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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Advanced processing for high-bandwidth sensor systems

Advanced processing for high-bandwidth sensor systems

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conference on Imaging Spectrometry vi

作者： Szymanski, JJ Blain, P Bloch, J Brislawn, C Brumby, S Caffrey, M Dunham, M Frigo, J Gokhale, M Harvey, N Kenyon, G Kim, W Layne, J Lavenier, D McCabe, K Mitchell, M Moore, K Perkins, S Porter, R Robinson, S Salazar, A Theiler, J Young, AC Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)0819437778

Compute performance and algorithm design are key problems of image processing and scientific computing in general. For example, imaging spectrometers are capable of producing data in hundreds of spectral bands with millions of pixels. These data sets show great promise for remote sensing applications, but require new and computationally intensive processing. The goal of the Deployable Adaptive Processing Systems (DAPS) project at Los Alamos National Laboratory is to develop advanced processing hardware and algorithms for high-bandwidth sensor applications. The project has produced electronics for processing multi- and hyper-spectral sensor data, as well as LIDAR data, while employing processing elements using a variety of technologies. The project team is currently working on reconfigurable computing technology and advanced feature extraction techniques, with an emphasis on their application to image and RF signal processing. This paper presents reconfigurable computing technology and advanced feature extraction algorithm work and their application to multi- and hyperspectral image processing. Related projects on genetic algorithms as applied to image processing will be introduced, as will the collaboration between the DAPS project and the DARPA Adaptive computing Systems program. Further details are presented in other talks during this conference and in other conferences taking place during this symposium.

关键词： multispectral analysis hyperspectral analysis image processing reconfigurable computing remote sensing

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Optimization for viewshed analysis on GPU

Optimization for viewshed analysis on GPU

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International conference on Geoinformatics

作者： Gao, Yong Yu, Hao Liu, Yu Liu, Yuehu Liu, Mingchao Zhao, Yong Institute of Remote Sensing and Geographic Information System Peking University Beijing China

ISBN: (纸本)9781612848488

Different algorithms have been raised for viewshed analysis and measures were taken to get the compromise between performance and accuracy. The most accurate and standard algorithm is still the basic interpolation method, though its time cost is high. However, the development of Graphic Processing Unit (GPU) enables us to acquire high performance with normal PC, especially when the Compute Unified Device Architecture (CUDA) is put forward by NviDIA for general purpose computing. In this paper, we will analyze the feasibility to map the basic interpolation method into GPU application and give our approach to achieve this goal. Further, we will introduce two critical measures in this approach: one is how to assign the data into different memory spaces on GPU according to their different access characteristics;the other is how to regularize the computing instructions and minimize branch parts in the procedure. At most, nearly 70 times speedup is reached in the experiment compared with the basic interpolation method on CPU. © 2011 IEEE.

关键词： Graphics processing unit

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Radar & Additive Manufacturing Technologies: the Future of Internet of Things (IoT)

Radar & Additive Manufacturing Technologies: the Future of I...

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IEEE Radar conference

作者： Hester, Jimmy G. D. Kimionis, John Bahr, Ryan Su, Wenjing Tehrani, Bijan Tentzeris, Manos M. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9781538641675

The authors make a case for a soon-to-be preponderance of radar technologies for communications with devices of the IoT. First, the tag architectures enabling these communications are presented. Novel 24 GHz and mm-wave flexible low-cost chip less RFID and backscatter/reflectarray-based node approaches demonstrating a record measured range of 80m, with a potential maximum range in excess of 1 km, zero to low W power consumption, and Gbps communications rates are presented. In a second part, recent advances in additive-manufacturing technologies enabling large cost reductions for the next generation of integrated radar ICs are reported. Specifically, the combination of 3D and inkjet printing enables the development of fully 3D compact low-cost integrated modules along with high-performance interconnects. These innovations, simultaneously empowering and empowered by radar technologies, present a compelling avenue for the emergence of dense constellations of flexible smart skin nodes for ubiquitous sensing, identification, and localization.

关键词： remote sensing mm-wave Chipless RFID Inkjet printing Flexible electronics Internet of Things Smart Skin

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