An experimental comparison of parallel algorithms for hyperspectral analysis using heterogeneous and homogeneous networks of workstations

IEEE Geosci. Remote Sens. Mag.

Camps‐Valls

et al. 2013

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1,689

766

Abstract-Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

Section: A Clusters and Distributed Platforms For Hyperspectral Procmentioning

confidence: 99%

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Bioucas-Dias¹,

IEEE Geosci. Remote Sens. Mag.

Camps‐Valls

et al. 2013

Self Cite

1,689

766

“…The main advantage of the spatial-domain decomposition approach in Figure 3 is that the cost of inter-processor communication is reduced, as shown in the previous work [10,18,24]. At this point, it is important to emphasize that spatial-domain partitioning should be used with extra care when parallelizing data processing techniques that include sample spectral correlation and/or covariance calculations, such as the principal component transform [28] or the RX detector developed by Reed and Yu [29], both of them widely used in hyperspectral image analysis.…”

Section: Hyperspectral Data Partitioningmentioning

confidence: 79%

“…c ij = c ji ). With the above assumptions in mind [24], processor p i should accomplish a share of i · W of the total workload, denoted by W , to be performed by a certain algorithm, with i ≥0 for 1≤i≤P and P i=1 i = 1, being P the total number of processors in the system.…”

Section: Optimization Problemmentioning

confidence: 99%

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

Concurrency and Computation

Paz

2009

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SUMMARYThe 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.

“…the latency for large message sizes (typical in hyperspectral imaging applications, since each pixel vector is made up of hundreds of spectral values) is extremely sensitive to the size of the message. Thus, decreasing the size of the messages could significantly reduce the latency and hence improve the scalability or parallel performance [5]. In this paper, we propose a new framework based on the utilization of lossy data compression techniques for improving the scalability of parallel hyperspectral imaging algorithms on both homogeneous and heterogeneous parallel computers.…”

Section: Introductionmentioning

confidence: 99%

Improving the scalability of parallel algorithms for hyperspectral image analysis using adaptive message compression

2009 IEEE International Geoscience and Remote Sensing Symposium

Paz

2009

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In previous work, we have reported that the scalability of parallel processing algorithms for hyperspectral image analysis is affected by the amount of data to exchanged through the communication network of the parallel system. However, large messages are common in hyperspectral imaging applications since processing algorithms are often pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of data compression techniques for improving the scalability of a standard spectral unmixin-based parallel hyperspectral processing chain on heterogeneous networks of workstations. Our experimental results indicate that adaptive, wavelet-based lossy compression can lead to improvements in the scalability of the parallel algorithms without significantly sacrificing algorithm analysis accuracy.