High-performance computing in remotely sensed hyperspectral imaging: The pixel purity index algorithm as a case study (original) (raw)
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Journal of Signal Processing Systems, 2010
Advances in sensor technology are revolutionizing the way remotely sensed data is collected, managed and analyzed. The incorporation of latestgeneration sensors to airborne and satellite platforms is currently producing a nearly continual stream of highdimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral signal processing is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. Many current and future applications of remote sensing in Earth science, space science, and soon in exploration science will require (near) realtime processing capabilities. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) systems and architectures in remote sensing missions. With the aim of providing an overview of current and new trends in parallel and distributed systems for remote sensing applications, this paper explores three HPC-based paradigms for efficient implementation of the Pixel Purity
Recent Developments in High Performance Computing for Remote Sensing: A Review
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011
Remote sensing data have become very widespread in recent years, and the exploitation of this technology has gone from developments mainly conducted by government intelligence agencies to those carried out by general users and companies. There is a great deal more to remote sensing data than meets the eye, and extracting that information turns out to be a major computational challenge. For this purpose, high performance computing (HPC) infrastructure such as clusters, distributed networks or specialized hardware devices provide important architectural developments to accelerate the computations related with information extraction in remote sensing. In this paper, we review recent advances in HPC applied to remote sensing problems; in particular, the HPC-based paradigms included in this review comprise multiprocessor systems, large-scale and heterogeneous networks of computers, grid and cloud computing environments, and hardware systems such as field programmable gate arrays (FPGAs) and graphics processing units (GPUs). Combined, these parts deliver a snapshot of the state-of-the-art and most recent developments in those areas, and offer a thoughtful perspective of the potential and emerging challenges of applying HPC paradigms to remote sensing problems.
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.
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.
2005
Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied by systems like the airborne visible infra-red imaging spectrometer (AVIRIS), developed by NASA Jet Propulsion Laboratory, allows their exploitation in diverse applications, such as detection and control of wild fires and hazardous agents in water and atmosphere, detection of military targets and management of natural resources. Even though the above applications require a response in real time, few solutions are available to provide fast and efficient analysis of these types of data. This is mainly caused by the dimensionality of hyperspectral images, which limits their exploitation in analysis scenarios where the spatial and temporal requirements are very high. In the present work, we describe a new parallel methodology which deals with most of the previously addressed problems. The computational performance of the proposed analysis methodology is evaluated using two parallel computer systems, a SGI Origin 2000 shared memory system located at the European Center of Parallelism of Barcelona, and the Thunderhead Beowulf cluster at NASA's Goddard Space Flight Center.
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.
2006
Hyperspectral imaging is a new technique which has become increasingly important in many remote sensing applications, including automatic target recognition for military and defense/security deployment, risk/hazard prevention and response including wild land fire tracking, biological threat detection, monitoring of oil spills and other types of chemical contamination, etc. Hyperspectral imaging applications generate massive volumes of data and require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. Although most currently available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of workstations represent a very promising cost-effective solution expected to play a major role in the design of highperformance computing platforms for many ongoing and planned remote sensing missions. This paper explores innovative techniques for mapping hyperspectral analysis algorithms onto heterogeneous networks of workstations available at NASA's Goddard Space Flight Center and University of Maryland. Experimental results reveal that heterogeneous networks of workstations represent a source of computational power that is both accessible and applicable in hyperspectral imaging studies.
Parallel Hyperspectral Image Processing on Commodity Graphics Hardware
2006
Many recent research efforts have been devoted to the use of commodity hardware for solving computationallyintensive scientific problems. Among such problems, hyperspectral imaging has created new processing challenges in the remote sensing community. Hyperspectral sensors are now capable of collecting hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. For instance, NASA is continuously gathering high-dimensional image data with hyperspectral sensors such as Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS).
International Journal of High Performance Computing Applications, 2008
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 on-going 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.
Parallel Computing, 2008
Imaging spectroscopy, also known as hyperspectral imaging, is a new technique that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. In particular, NASA is continuously gathering high-dimensional image data from the surface of the earth with hyperspectral sensors such as the Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) or the Hyperion hyperspectral imager aboard NASA's Earth Observing-1 (EO-1) spacecraft. Despite the massive volume of scientific data commonly involved in hyperspectral imaging applications, very few parallel strategies for hyperspectral analysis are currently available, and most of them have been designed in the context of homogeneous computing platforms. However, heterogeneous networks of workstations represent a very promising cost-effective solution that is expected to play a major role in the design of high-performance computing platforms for many on-going and planned remote sensing missions. Our main goal in this paper is to understand parallel performance of hyperspectral imaging algorithms comprising the standard hyperspectral data processing chain (which includes pre-processing, selection of pure spectral components and linear spectral unmixing) in the context of fully heterogeneous computing platforms. For that purpose, we develop an exhaustive quantitative and comparative analysis of several available and new parallel hyperspectral imaging algorithms by comparing their efficiency on both a fully heterogeneous network of workstations and a massively parallel homogeneous cluster at NASA's Goddard Space Flight Center in Maryland.