Commodity cluster-based parallel processing of hyperspectral imagery

The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral Earth observing sensors such as the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of fast techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. This paper describes commodity cluster-based parallel data analysis strategies for hyperspectral imagery, a new class of image data that comprises hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. An unsupervised technique that integrates the spatial and spectral information in the image data using multi-channel morphological transformations is parallelized and compared to other available parallel algorithms. The code's portability, reusability and scalability are illustrated by using two high-performance parallel computing architectures: a distributed memory, multiple instruction multiple data (MIMD)-style multicomputer at European Center for Parallelism of Barcelona, and a Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results suggest that Beowulf clusters are a source of computational power that is both accessible and applicable to obtaining results in valid response times in information extraction applications from hyperspectral imagery.     

Parallel processing of remotely sensed hyperspectral imagery: full‐pixel versus mixed‐pixel classification

The rapid development of space and computer technologies allows for the possibility to store huge amounts of remotely sensed image data, collected using airborne and satellite instruments. In particular, NASA is continuously gathering high‐dimensional image data with Earth observing hyperspectral sensors such as the Jet Propulsion Laboratory's airborne visible–infrared imaging spectrometer (AVIRIS), which measures reflected radiation in hundreds of narrow spectral bands at different wavelength channels for the same area on the surface of the Earth. The development of fast techniques for transforming massive amounts of hyperspectral data into scientific understanding is critical for space‐based Earth science and planetary exploration. Despite the growing interest in hyperspectral imaging research, only a few efforts have been devoted to the design of parallel implementations in the literature, and detailed comparisons of standardized parallel hyperspectral algorithms are currently unavailable. This paper compares several existing and new parallel processing techniques for pure and mixed‐pixel classification in hyperspectral imagery. The distinction of pure versus mixed‐pixel analysis is linked to the considered application domain, and results from the very rich spectral information available from hyperspectral instruments. In some cases, such information allows image analysts to overcome the constraints imposed by limited spatial resolution. In most cases, however, the spectral bands collected by hyperspectral instruments have high statistical correlation, and efficient parallel techniques are required to reduce the dimensionality of the data while retaining the spectral information that allows for the separation of the classes. In order to address this issue, this paper also develops a new parallel feature extraction algorithm that integrates the spatial and spectral information. The proposed technique is evaluated (from the viewpoint of both classification accuracy and parallel performance) and compared with other parallel techniques for dimensionality reduction and classification in the context of three representative application case studies: urban characterization, land‐cover classification in agriculture, and mapping of geological features, using AVIRIS data sets with detailed ground‐truth. Parallel performance is assessed using Thunderhead, a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. The detailed cross‐validation of parallel algorithms conducted in this work may specifically help image analysts in selection of parallel algorithms for specific applications. Copyright © 2008 John Wiley & Sons, Ltd.     

Impact of platform heterogeneity on the design of parallel algorithms for morphological processing of high-dimensional image data

The main objective of this paper is to describe a realistic framework to understand parallel performance of high-dimensional image processing algorithms in the context of heterogeneous networks of workstations (NOWs). As a case study, this paper explores techniques for mapping hyperspectral image analysis techniques onto fully heterogeneous NOWs. Hyperspectral imaging is a new technique in remote sensing that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. The automation of techniques able to transform massive amounts of hyperspectral data into scientific understanding in valid response times is critical for space-based Earth science and planetary exploration. Using an evaluation strategy which is based on comparing the efficiency achieved by an heterogeneous algorithm on a fully heterogeneous NOW with that evidenced by its homogeneous version on a homogeneous NOW with the same aggregate performance as the heterogeneous one, we develop a detailed analysis of parallel algorithms that integrate the spatial and spectral information in the image data through mathematical morphology concepts. For comparative purposes, performance data for the tested algorithms on Thunderhead (a large-scale Beowulf cluster at NASA’s Goddard Space Flight Center) are also provided. Our detailed investigation of the parallel properties of the proposed morphological algorithms provides several intriguing findings that may help image analysts in selection of parallel techniques and strategies for specific applications.

PORTING REGULAR APPLICATIONS ON HETEROGENEOUS WORKSTATION NETWORKS: PERFORMANCE ANALYSIS AND MODELING

Abstract

Heterogeneous networks of workstations and/or personal computers (NOW) are increasingly used as a powerful platform for the execution of parallel applications. When applications previously developed for traditional parallel machines (homogeneous and dedicated) are ported to NOWs, performance worsens owing in part to less efficient communications but more often to unbalancing.

In this paper, we address the problem of the efficient porting to heterogeneous NOWs of data-parallel applications originally developed using the SPMD paradigm for homogeneous parallel systems with regular topology like ring.

To achieve good performance, the computation time on the various machines composing the NOW must be as balanced as possible. This can be obtained in two ways: by using an heterogeneous data partition strategy with a single process per node, or by splitting homogeneously data among processes and assigning to each node a number of processes proportional to its computing power. The first method is however more difficult, since some modifications in the code are always needed, whereas the second approach requires very few changes.

We carry out a simplified but reliable analysis, and propose a simple model able to simulate performance in the various situations. Two test cases, matrix multiplication and computation of long-range interactions, are considered, obtaining a good agreement between simulated and experimental results.

Our analysis shows that an efficient porting of regular homogeneous data-parallel applications on heterogeneous NOWs is possible. Particularly, the approach based on multiple processes per node turns out to be a straightforward and effective way for achieving very satisfying performance in almost all situations, even dealing with highly heterogeneous systems.     

Integrating task parallelism in data parallel languages for parallel programming on NOWs

A number of high‐level parallel programming platforms for networks of workstations (NOWs) have been developed in recent times. Most of these platforms target the exploitation of data parallelism in applications. They do not allow expressibility of applications as a collection of tasks along with their precedence relationships. As a result, the control or task parallelism in an application cannot be expressed or exploited. The current work aims at integrating the notion of task parallelism and precedence relationships among constituting tasks to such high‐level data parallel platforms for NOWs. Our model of integration provides for arbitrary nesting of data and task parallel modules. Also, the precedence relationships are clearly reflected from the program structure. The model relieves the programmer from the need to design applications for non‐determinism in the order of completion of constituting tasks. The design of the runtime support as well as system‐level book keeping is discussed. The model is general enough to be applied to a wide range of data parallel platforms. A specific case of integrating the model into anonymous remote computing (ARC), a data parallel programming platform, is presented. The performance related aspects are also discussed. Copyright © 2000 John Wiley & Sons, Ltd.     

Parallel heterogeneous CBIR system for efficient hyperspectral image retrieval using spectral mixture analysis

The purpose of content‐based image retrieval (CBIR) is to retrieve, from real data stored in a database, information that is relevant to a query. In remote sensing applications, the wealth of spectral information provided by latest‐generation (hyperspectral) instruments has quickly introduced the need for parallel CBIR systems able to effectively retrieve features of interest from ever‐growing data archives. To address this need, this paper develops a new parallel CBIR system that has been specifically designed to be run on heterogeneous networks of computers (HNOCs). These platforms have soon become a standard computing architecture in remote sensing missions due to the distributed nature of data repositories. The proposed heterogeneous system first extracts an image feature vector able to characterize image content with sub‐pixel precision using spectral mixture analysis concepts, and then uses the obtained feature as a search reference. The system is validated using a complex hyperspectral image database, and implemented on several networks of workstations and a Beowulf cluster at NASA's Goddard Space Flight Center. Our experimental results indicate that the proposed parallel system can efficiently retrieve hyperspectral images from complex image databases by efficiently adapting to the underlying parallel platform on which it is run, regardless of the heterogeneity in the compute nodes and communication links that form such parallel platform. Copyright © 2009 John Wiley & Sons, Ltd.     

Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest

It has been suggested that attempts to use remote sensing to map the spatial and structural patterns of individual tree species abundances in heterogeneous forests, such as those found in northeastern North America, may benefit from the integration of hyperspectral or multi-spectral information with other active sensor data such as lidar. Towards this end, we describe the integrated and individual capabilities of waveform lidar and hyperspectral data to estimate three common forest measurements - basal area (BA), above-ground biomass (AGBM) and quadratic mean stem diameter (QMSD) - in a northern temperate mixed conifer and deciduous forest. The use of this data to discriminate distribution and abundance patterns of five common and often, dominant tree species was also explored. Waveform lidar imagery was acquired in July 2003 over the 1000 ha. Bartlett Experimental Forest (BEF) in central New Hampshire (USA) using NASA's airborne Laser Vegetation Imaging Sensor (LVIS). High spectral resolution imagery was likewise acquired in August 2003 using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Field data (2001-2003) from over 400 US Forest Service Northern Research Station (USFS NRS) plots were used to determine actual site conditions.Results suggest that the integrated data sets of hyperspectral and waveform lidar provide improved outcomes over use of either data set alone in evaluating common forest metrics. Across all forest conditions, 8-9% more of the variation in AGBM, BA, and QMSD was explained by use of the integrated sensor data in comparison to either AVIRIS or LVIS metrics applied singly, with estimated error 5-8% lower for these variables. Notably, in an analysis using integrated data limited to unmanaged forest tracts, AGBM coefficients of determination improved by 25% or more, while corresponding error levels decreased by over 25%. When data were restricted based on the presence of individual tree species within plots, AVIRIS data alone best predicted species-specific patterns of abundance as determined by species fraction of biomass. Nonetheless, use of LVIS and AVIRIS data - in tandem - produced complementary maps of estimated abundance and structure for individual tree species, providing a promising adjunct to traditional forest inventory and conservation biology planning efforts.     

工作站网络环境下的并行计算

当前工作站网络环境（ＮＯＷｓ）下高性能科学与工程计算是并行计算的一个热门话题，本文借助于ＬｏｇＰ并行计算模型，提出了一套新的效率评价准则，用于优化并行算法效率以达到最佳实现效果，揭示了影响算法并行效率发挥的关键因素，并从算法和程序设计角度提出了相应措施，探讨了急需解决的几个关键性问题，三个典型应用问题的数值实验结果文中给出。     

Parallel patterns for heterogeneous CPU/GPU architectures: Structured parallelism from cluster to cloud

The widespread adoption of traditional heterogeneous systems has substantially improved the computing power available and, in the meantime, raised optimisation issues related to the processing of task streams across both CPU and GPU cores in heterogeneous systems. Similar to the heterogeneous improvement gained in traditional systems, cloud computing has started to add heterogeneity support, typically through GPU instances, to the conventional CPU-based cloud resources. This optimisation of cloud resources will arguably have a real impact when running on-demand computationally-intensive applications.In this work, we investigate the scaling of pattern-based parallel applications from physical, “local” mixed CPU/GPU-clusters to a public cloud CPU/GPU infrastructure. Specifically, such parallel patterns are deployed via algorithmic skeletons to exploit a peculiar parallel behaviour while hiding implementation details.We propose a systematic methodology to exploit approximated analytical performance/cost models, and an integrated programming framework that is suitable for targeting both local and remote resources to support the offloading of computations from structured parallel applications to heterogeneous cloud resources, such that performance values not available on local resources may be actually achieved with the remote resources. The amount of remote resources necessary to achieve a given performance target is calculated through the performance models in order to allow any user to hire the amount of cloud resources needed to achieve a given target performance value. Thus, it is therefore expected that such models can be used to devise the optimal proportion of computations to be allocated on different remote nodes for Big Data computations.We present different experiments run with a proof-of-concept implementation based on FastFlow  on small departmental clusters as well as on a public cloud infrastructure with CPU and GPU using the Amazon Elastic Compute Cloud. In particular, we show how CPU-only and mixed CPU/GPU computations can be offloaded to remote cloud resources with predictable performances and how data intensive applications can be mapped to a mix of local and remote resources to guarantee optimal performances.     

Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada

This study investigated the potential value of integrating hyperspectral visible, near-infrared, and short-wave infrared imagery with multispectral thermal data for geological mapping. Two coregistered aerial data sets of Cuprite, Nevada were used: Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data, and MODIS/ASTER Airborne Simulator (MASTER) multispectral thermal data. Four classification methods were each applied to AVIRIS, MASTER, and a combined set. Confusion matrices were used to assess the classification accuracy. The assessment showed, in terms of kappa coefficient, that most classification methods applied to the combined data achieved a marked improvement compared to the results using either AVIRIS or MASTER thermal infrared (TIR) data alone. Spectral angle mapper (SAM) showed the best overall classification performance. Minimum distance classification had the second best accuracy, followed by spectral feature fitting (SFF) and maximum likelihood classification. The results of the study showed that SFF applied to the combination of AVIRIS with MASTER TIR data are especially valuable for identification of silicified alteration and quartzite, both of which exhibit distinctive features in the TIR region. SAM showed some advantages over SFF in dealing with multispectral TIR data, obtaining higher accuracy in discriminating low albedo volcanic rocks and limestone which do not have unique, distinguishing features in the TIR region.     

Unmixing-based content retrieval system for remotely sensed hyperspectral imagery on GPUs

This paper presents a new unmixing-based retrieval system for remotely sensed hyperspectral imagery. The need for this kind of system is justified by the exponential growth in the volume and number of remotely sensed data sets from the surface of the Earth. This is particularly the case for hyperspectral images, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels. To deal with the high computational cost of extracting the spectral information needed to catalog new hyperspectral images in our system, we resort to efficient implementations of spectral unmixing algorithms on commodity graphics processing units (GPUs). Spectral unmixing is a very popular approach for interpreting hyperspectral data with sub-pixel precision. This paper particularly focuses on the design of the proposed framework as a web service, as well as on the efficient implementation of the system on GPUs. In addition, we present a comparison of spectral unmixing algorithms available in the system on both CPU and GPU architectures.     

The signal-to-noise ratio (SNR) required for the estimation of foliar biochemical concentrations

Abstract This paper compares estimates of the signal-to-noise ratio(SNR) required by imaging spectrometers for the estimation of foliar biochemical concentrations and the SNR currently achieved by the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS). The work was comprised of three sections. Section 1: the SNR required by imaging spectrometers was estimated by modelling three data sets, each of which more closely approximated the data recorded by the AVIRIS. The remaining stages were concerned with estimating the SNR currently achieved by the AVIRIS. Section 2: SNR estimates made as part of instrument calibration were scaled to those that would be expected when viewing vegetation, and section 3: SNR was estimated directly from AVIRIS imagery. The results of these three sections were then compared to assess the SNR performance of the AVIRIS and its utility for the estimation of foliar biochemical concentrations. The SNR of the AVIRIS is planned to double between 1994-5 and while this sensor was barely adequate for the estimation of foliar biochemical concentrations in 1992-3 it should be more than adequate from 1995 onwards.     

Hyperspectral change detection: an experimental comparative study

ABSTRACT

The Earth’s surface is constantly changing due to variations originating from the increasing human population. In the last decade, numerous methods were presented in the literature for change detection using multispectral image data. Owing to the increasing availability of hyperspectral images, these methods are now being applied to hyperspectral images. The main objective of this study is to present different change detection methods in hyperspectral imagery. Numerous algorithms (more than 43 algorithms) have been proposed for change detection in hyperspectral imagery over the last decade. In this work, we provide a comparative review of these algorithms through experimental results. We place the algorithms in five major groups: (1) match-based, (2) transformation-based, (3) direct classification-based, (4) post-classification-based, and (5) hybrid-based. We evaluate and compare the performances of all five groups using two real-world data sets of multi-temporal hyperspectral imagery. This comparative study investigates the advantages and disadvantages of the effects of preprocessing steps in the efficiency of the hyperspectral change detection (HSCD) methods. These preprocessing steps are considered in four scenarios, including: (1) considering only spatial or geometric correction without noise reduction and spectral correction; (2) spatial, atmospheric, and radiometric corrections without noise reduction; (3) spatial correction and noise reduction without atmospheric and radiometric corrections; and (4) spatial, atmospheric, and radiometric correction with noise reduction. The empirical results, followed by a summary of the pros and cons of each algorithm, aim to help researchers select the procedures with the best characteristics for HSCD applications.     

基于CPU/GPU异构模式的高光谱遥感影像数据处理研究与实现

近年来,基于GPU的新型异构高性能计算模式的蓬勃发展为众多领域应用提供了良好的发展机遇,国内外遥感专家开始引入高性能异构计算来解决高光谱遥感影像高维空间特点所带来的数据计算量大、实时处理难等问题。在此简要介绍了高光谱遥感和CPU/GPU异构计算模式,总结了近几年国内外基于CPU/GPU异构模式的高光谱遥感数据处理研究现状和问题;并面向共享存储型小型桌面超级计算机,基于CPU/GPU异构模式实现了高光谱遥感影像MNF降维的并行化,通过与串行程序和共享存储的OpenMP同构模式对比,验证了异构模式在高光谱遥感处理领域的发展潜力。     

On Endmember Identification in Hyperspectral Images Without Pure Pixels: A Comparison of Algorithms

Hyperspectral imaging is an active area of research in Earth and planetary observation. One of the most important techniques for analyzing hyperspectral images is spectral unmixing, in which mixed pixels (resulting from insufficient spatial resolution of the imaging sensor) are decomposed into a collection of spectrally pure constituent spectra, called endmembers weighted by their correspondent fractions, or abundances. Over the last years, several algorithms have been developed for automatic endmember extraction. Many of them assume that the images contain at least one pure spectral signature for each distinct material. However, this assumption is usually not valid due to spatial resolution, mixing phenomena, and other considerations. A?recent trend in the hyperspectral imaging community is to design endmember identification algorithms which do not assume the presence of pure pixels. Despite the proliferation of this kind of algorithms, many of which are based on minimum enclosing simplex concepts, a rigorous quantitative and comparative assessment is not yet available. In this paper, we provide a comparative analysis of endmember extraction algorithms without the pure pixel assumption. In our experiments we use synthetic hyperspectral data sets (constructed using fractals) and real hyperspectral scenes collected by NASA’s Jet Propulsion Laboratory.     

基于多态蚁群算法的高光谱遥感影像最优波段选择

由于传统蚁群算法搜索空间大,算法时间复杂度高等,导致基于传统蚁群算法的高光谱数据波段选择算法(ACA-BS)耗时长,算法效率低下,且易陷入局部最优。而多态蚁群算法能大大缩小算法的搜索空间,降低算法时间复杂度。因此,研究设计了基于多态蚁群算法的高光谱数据波段选择算法(PACA-BS)。从算法运行时间、波段子集的类别可分性及信息量、总体分类精度等方面对算法进行对比分析。用于实验的数据为Hyperion和AVIRIS高光谱影像。实验结果表明:PACA-BS的运行时间较ACA-BS大大减少;对Hyperion影像进行降维时,基于PACA-BS的运行时间约为ACA-BS的一半。两种算法获得的波段子集的类别可分性大小较为接近,但PACA-BS获得的波段子集的信息量和总体分类精度优于ACA-BS。研究表明PACA-BS是一种效率较高的高光谱波段选择算法。     

一个用于工作站网络的动态负载平衡算法

数学和科学计算中的大部分问题都可以用数据并行程序来开发其并行性,但是在工作站网络环境中,负载波动很大,负载平衡是影响其效率的一个重要因素。本文提出了一种动态负载平衡的算法,它可以使数据并行程序在运行时动态地调整负载。并且文中给出了这种算法的实验结果。     

平行数据:从大数据到数据智能*

为了解决实际问题,大数据分析处理系统需要获取数据,然而实际场景中收集到的实际数据通常不完备.另外,大多数问题的解决方案通常是由问题引导或者仅仅进行数据分析,运行参数调整和设定带有较大的盲目性,难以达到应用的智能性.为此,文中提出平行数据的概念和框架,根据实际数据经计算实验产生真正的虚拟大数据,结合默顿定律,以期待的解决方案与问题进行广义对偶,引导大数据聚焦到实际问题.实际数据与虚拟数据动态互动,平行演化,形成一个虚实相生、数据动态变化的过程,最终使数据具备智能,进而解决未知的问题.平行数据不但是一种数据表示形式,更是一种数据演化机制与方式,其特色是虚实互动,所有数据的动力学轨迹构成了数据动力学系统.平行数据为数据处理、表示、挖掘和应用提供了一个新的范式.     

Spectral unmixing of vegetation,soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations

Remote measurements of the fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil are critical to understanding climate and land-use controls over the functional properties of arid and semi-arid ecosystems. Spectral mixture analysis is a method employed to estimate PV, NPV and bare soil extent from multispectral and hyperspectral imagery. To date, no studies have systematically compared multispectral and hyperspectral sampling schemes for quantifying PV, NPV and bare soil covers using spectral mixture models. We tested the accuracy and precision of spectral mixture analysis in arid shrubland and grassland sites of the Chihuahuan Desert, New Mexico, USA using the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). A general, probabilistic spectral mixture model, Auto-MCU, was developed that allows for automated sub-pixel cover analysis using any number or combination of optical wavelength samples. The model was tested with five different hyperspectral sampling schemes available from the AVIRIS data as well as with data convolved to Landsat TM, Terra MODIS, and Terra ASTER optical channels. Full-range (0.4-2.5 w m) sampling strategies using the most common hyperspectral or multispectral channels consistently over-estimated bare soil extent and under-estimated PV cover in our shrubland and grassland sites. This was due to bright soil reflectance relative to PV reflectance in visible, near-IR, and shortwave-IR channels. However, by utilizing the shortwave-IR2 region (SWIR2; 2.0-2.3 w m) with a procedure that normalizes all reflectance values to 2.03 w m, the sub-pixel fractional covers of PV, NPV and bare soil constituents were accurately estimated. AVIRIS is one of the few sensors that can provide the spectral coverage and signal-to-noise ratio in the SWIR2 to carry out this particular analysis. ASTER, with its 5-channel SWIR2 sampling, provides some means for isolating bare soil fractional cover within image pixels, but additional studies are needed to verify the results.     

谐波分析光谱角制图高光谱影像分类

目的 针对光谱角制图(SAM)分类算法对高光谱像元光谱曲线的局部特征和其辐射强度不敏感,而且易受噪声和维数灾难影响,致使分类效率低和精度较差等缺陷,将谐波分析(HA)技术引入到SAM高光谱影像分类中,提出一种基于谐波分析的光谱角制图(HA-SAM)高光谱影像分类算法.方法 利用HA技术将高光谱影像从光谱维变换到能量谱特征维空间,并提取低次谐波分量及特征系数(谐波余项、相位和振幅),用特征系数组成的向量代替光谱向量,对高光谱影像进行SAM分类.结果 将SAM和HA-SAM同时应用于EO-1卫星的Hyperion高光谱影像分类,通过对比和分析,验证了HA-SAM的优越性,再选择AVIRIS(airborne visible infrared imaging spectrometer)高光谱影像对HA-SAM进行验证,结果表明该算法具有较强的普适性.结论 HA-SAM提高了传统SAM高光谱影像分类的效率和精度,而且适用性较强具有良好的应用前景.