Massively parallel processing of remotely sensed hyperspectral images (original) (raw)
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Parallel Implementation of Hyperspectral Image Processing Algorithms
2006
High computing performance of algorithm analysis is essential in many hyperspectral imaging applications, including automatic target recognition for homeland defense and security, risk/hazard prevention and monitoring, wild-land fire tracking and biological threat detection. Despite the growing interest in hyperspectral imaging research, only a few efforts devoted to designing and implementing well-conformed parallel processing solutions currently exist in the open literature. With the recent explosion in the amount and dimensionality of hyperspectral imagery, parallel processing is expected to become a requirement in most remote sensing missions. In this paper, we take a necessary first step towards the quantitative comparison of parallel techniques and strategies for analyzing hyperspectral data sets. Our focus is on three types of algorithms: automatic target recognition, spectral mixture analysis and data compression. Three types of high performance computing platforms are used for demonstration purposes, including commodity cluster-based systems, heterogeneous networks of distributed workstations and hardware-based computer architectures. Combined, these parts deliver a snapshot of the state of the art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of incorporating parallel computing models into hyperspectral remote sensing problems.
2008
The wealth spatial and spectral information available from last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spatial arrangement. In thematic classification applications, however, the integration of spatial and spectral information can be greatly beneficial. Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters, low-cost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice for dealing with the massive amount of image data produced by Earth observation missions. In this paper, we develop a new morphological/neural algorithm for parallel classification of high-dimensional (hyperspectral) remotely sensed image data sets. The algorithm's accuracy and parallel performance is tested in a variety of homogeneous and heterogeneous computing platforms, using two networks of workstations distributed among different locations, and also a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center in Maryland.
Chapter 7 Parallel Implementation of Morphological Neural Networks for Hyperspectral Image Analysis
2012
Improvement of spatial and spectral resolution in latest-generation Earth observation instruments is introducing extremely high computational requirements in many remote sensing applications. While thematic classification applications have greatly benefited from this increasing amount of information, new computational requirements have been introduced, in particular, for hyperspectral image data sets with 131 132 High-Performance Computing in Remote Sensing hundreds of spectral channels and very fine spatial resolution. Low-cost parallel computing architectures such as heterogeneous networks of computers have quickly become a standard tool of choice for dealing with the massive amount of image data sets. In this chapter, a new parallel classification algorithm for hyperspectral imagery based on morphological neural networks is presented and discussed. The parallel algorithm is mapped onto heterogeneous and homogeneous parallel platforms using a hybrid partitioning scheme. In order to test the accuracy and parallel performance of the proposed approach, we have used two networks of workstations distributed among different locations, and also a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center in Maryland. Experimental results are provided in the context of a real agriculture and farming application, using hyperspectral data acquired by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRS), operated by the NASA Jet Propulstion Laboratory, over the valley of Salinas in California.
Parallel Classification of Hyperspectral Images Using Neural Networks
Studies in Computational Intelligence
Neural networks represent a widely used alternative to deal with remotely sensed image data. The improvement of spatial and spectral resolution in latestgeneration Earth observation instruments is expected to introduce extremely high computational requirements in neural network-based algorithms for classification of high-dimensional data sets such as hyperspectral images, with hundreds of spectral channels and very fine spatial resolution. A significant advantage of neural networks versus other types of processing algorithms for hyperspectral imaging is that they are inherently amenable for parallel implementation. As a result, they can benefit from advances in low-cost parallel computing architectures such as heterogeneous networks of computers, which have soon become a standard tool of choice for dealing with the massive amount of image data sets. In this chapter, several techniques for classification of hyperspectral imagery using neural networks are presented and discussed. Experimental results are provided from the viewpoint of both classification accuracy and parallel performance on a variety of parallel computing platforms, including two networks of workstations at University of Maryland and a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center in Maryland. Two different application areas are addressed for demonstration: land-cover classification using hyperspectral data collected by NASA over the valley of Salinas, California, and urban city classification using data collected by the German Aerospace Agency (DLR) over the city of Pavia, Italy.
2009
Automatic target detection in hyperspectral images is a task that has attracted a lot of attention recently. In the last few years, several algoritms have been developed for this purpose, including the well-known RX algorithm for anomaly detection, or the automatic target detection and classification algorithm (ATDCA), which uses an orthogonal subspace projection (OSP) approach to extract a set of spectrally distinct targets automatically from the input hyperspectral data. Depending on the complexity and dimensionality of the analyzed image scene, the target/anomaly detection process may be computationally very expensive, a fact that limits the possibility of utilizing this process in time-critical applications. In this paper, we develop computationally efficient parallel versions of both the RX and ATDCA algorithms for near real-time exploitation of these algorithms. In the case of ATGP, we use several distance metrics in addition to the OSP approach. The parallel versions are quantitatively compared in terms of target detection accuracy, using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center in New York, five days after the terrorist attack of September 11th, 2001, and also in terms of parallel performance, using a massively Beowulf cluster available at NASA's Goddard Space Flight Center in Maryland.
PARALLELIZATION OF HYPERSPECTRAL IMAGING CLASSIFICATION AND DIMENSIONALITY REDUCTION ALGORITHMS By
2004
Hyperspectral imaging provides the capability to identify and classify materials remotely. The applications of such technology is applied everywhere from medical devices and military targets to environmental sciences. With the ongoing advances in spectrometers (spatial resolution and bits per pixel density) the data gathered is constantly increasing. Some hyperspectral imaging algorithms could easily take days or weeks in analyzing a full single hyperspectral data set. In this thesis we performed a porting and parallelization of four hyperspectral algorithms representative of the type of analysis done in a typical data set. Two of the algorithms are in the area of data classification, one in the area of feature reduction and the other one is a combination of both areas. The parallelized algorithms were benchmarked on the Intel 32 bits Pentium M architecture and the new Intel 64 bits Itanium 2 architecture. For three of the four algorithms we demonstrated that the use of parallel app...
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing, 2006
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 spacebased 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. MD. His research interests are in remote sensing, digital image analysis, parallel and distributed computing, hardware-based architectures, operating systems management and configuration, and neural network-based pattern recognition.
2006
Hyperspectral imaging is a new technique in remote sensing that generates hundreds of images corresponding to different wavelength channels for the same area on the surface of the Earth. Most available techniques for hyperspectral image classification focus on analyzing the data without incorporating the spatial information; i.e. the data is treated not as an image but as an unordered listing of spectral measurements where the spatial coordinates can be shuffled arbitrarily without affecting the final analysis. Despite the growing interest in the development of techniques for interpretation and classification of such high-dimensional imagery, only a few efforts devoted to the design of parallel implementations exist in the open literature. In this paper, we describe AMEEPAR, a parallel morphological algorithm that integrates the spatial and spectral information. The algorithm has been specifically optimized in this work for execution on heterogeneous networks of workstations. The parallel properties and classification accuracy of the proposed approach are evaluated using four networks of workstations distributed among different locations, and a massively parallel Beowulf cluster at NASA’s Goddard Space Flight Center.
2006
The development of efficient techniques for transforming the massive volume of remotely sensed hyperspectral data collected on a daily basis into scientific understanding is critical for space-based Earth science and planetary exploration. Although most available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of computers represent a promising cost-effective solution expected to play a major role in many on-going and planned remote sensing missions. To address the need for cost-effective parallel hyperspectral imaging algorithms, this paper develops an innovative heterogeneous parallel algorithm for spatial/spectral morphological analysis of hyperspectral image data. The algorithm has been developed using Heterogeneous MPI (HeteroMPI), an extension of MPI for programming high-performance computations on heterogeneous networks of computers. Experimental results are presented and discussed in the context of a realistic application, based on hyperspectral data collected by NASA's Jet Propulsion Laboratory.
Parallel Processing of Remotely Sensed Hyperspectral Images on Heterogeneous Clusters
2009
The development of efficient techniques for transforming massive volumes of remotely sensed hyperspectral data into scientific understanding is critical for space-based Earth science and planetary exploration. Although most available parallel processing strategies for information extraction and mining from hyperspectral imagery assume homogeneity in the underlying computing platform, heterogeneous networks of computers (HNOCs) have become a promising cost-effective solution, expected to play a major role in many ongoing and planned remote sensing missions. In this paper, we develop a new morphological parallel algorithm for hyperspectral image classification using HeteroMPI, an extension of MPI for programming high-performance computations on HNOCs. The main idea of HeteroMPI is to automate and optimize the selection of a group of processes that executes a heterogeneous algorithm faster than any other possible group in a heterogeneous environment. In order to analyze the impact of many-to-one (gather) communication operations introduced by our proposed algorithm, we resort to a recently proposed collective communication model. The parallel algorithm is validated using two heterogeneous clusters at University College Dublin and a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center.