Using blocks of skewers for faster computation of pixel purity index

Theiler, James; Lavenier, Dominique; Harvey, Neal R.; Perkins, Simon; Szymański, J.

doi:10.1117/12.406610

Cited by 29 publications

(21 citation statements)

References 7 publications

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“…N-FINDR and PPI were coded accordingly to [38] and [33], respectively. Regarding PPI, the number of skewers must be large [39], [40], [49]- [51]. Based on Monte Carlo runs, we concluded that the minimum number of skewers beyond which there is no unmixing improvements is about 1000.…”

Section: Evaluation Of the Vca Algorithmmentioning

confidence: 99%

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Vertex component analysis: a fast algorithm to unmix hyperspectral data

Nascimento

Dias

2005

IEEE Trans. Geosci. Remote Sensing

2,266

1,501

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Abstract-Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: 1) the endmembers are the vertices of a simplex and 2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.Index Terms-Linear unmixing, simplex, spectral mixture model, unmixing hypespectral data, unsupervised endmember extraction, vertex component analysis (VCA).

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Section: Evaluation Of the Vca Algorithmmentioning

confidence: 99%

“…The algorithm then projects every spectral vector onto skewers (large number of random vectors) [33], [40], [41]. The points corresponding to extremes, for each skewer direction, are stored.…”

Section: Introductionmentioning

confidence: 99%

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Nascimento

Dias

2005

IEEE Trans. Geosci. Remote Sensing

2,266

1,501

View full text Add to dashboard Cite

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“…8. A software approach for fast PPI was discussed by Theiler et al 80 which expanded a small set of "direct dot products" into a larger set of "derived dot products." Although this approach was originally designed as a software speedup, and as such was a "competitor" to the hardware implementation, Fig.…”

Section: Endmembers and Pixel Purity Indexmentioning

confidence: 99%

Co-design of software and hardware to implement remote sensing algorithms

et al. 2002

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Both for offline searches through large data archives and for onboard computation at the sensor head, there is a growing need for ever-more rapid processing of remote sensing data. For many algorithms of use in remote sensing, the bulk of the processing takes place in an "inner loop" with a large number of simple operations. For these algorithms, dramatic speedups can often be obtained with specialized hardware. The difficulty and expense of digital design continues to limit applicability of this approach, but the development of new design tools is making this approach more feasible, and some notable successes have been reported. On the other hand, it is often the case that processing can also be accelerated by adopting a more sophisticated algorithm design. Unfortunately, a more sophisticated algorithm is much harder to implement in hardware, so these approaches are often at odds with each other. With careful planning, however, it is sometimes possible to combine software and hardware design in such a way that each complements the other, and the final implementation achieves speedup that would not have been possible with a hardware-only or a software-only solution. We will in particular discuss the co-design of software and hardware to achieve substantial speedup of algorithms for multispectral image segmentation and for endmember identification.

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“…The user is thus enabled to explore the behavior of the algorithm under different conditions. Papers by Theiler et al 27,28 in this volume give more details on the project.…”

Section: Spectral Processing Applicationsmentioning

confidence: 99%

Advanced processing for high-bandwidth sensor systems

et al. 2000

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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.

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Using blocks of skewers for faster computation of pixel purity index

Cited by 29 publications

References 7 publications

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Co-design of software and hardware to implement remote sensing algorithms

Advanced processing for high-bandwidth sensor systems

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