A Statistical Approach to Improve Virtual Dimensionality of Hyperspectral Data

Vijayashekhar, S S; Bhatt, Jignesh S.; Chattopadhyay, Bhargab

doi:10.1109/igarss.2019.8899131

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Water absorption frequency bands 104-108, 150 to 163 and 220 have been removed to reduce the total frequency bands to 200 [27]. The number of endmembers p present in the Cuprite scene is 14 as given in [28] and in the Indian pines scene is 16 based on [29]. The virtual dimensionality estimates given by the Input pixel values are also scaled accordingly.…”

Section: Matlab Simulation Resultsmentioning

confidence: 99%

Improved Real-Time Fast Simplex Growing Algorithm for Finding Endmembers in Hyperspectral Data and its FPGA architecture

Ganjimala¹

2022

Preprint

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<p>This paper proposes a fast-processing algorithm for finding endmembers in hyperspectral image data and it’s FPGA architecture. The proposed algorithm named as Improved Real-Time Fast Simplex Growing Algorithm (IRT-FSGA) removes redundant computation for the pixels which do not qualify as endmember replacements and thereby achieves a significant reduction in computation time over the RT-FSGA algorithm. The improvement obtained is without compromising on the accuracy of the endmembers found. FPGA implementation results of the designed architecture show that the proposed IRT-FSGA achieves 33× improvement in performance at the cost of only a small area overhead. Thus, it is potentially useful in real-time applications which have stringent throughput and power requirements.</p>

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Section: Matlab Simulation Resultsmentioning

confidence: 99%

Improved Real-Time Fast Simplex Growing Algorithm for Finding Endmembers in Hyperspectral Data and its FPGA architecture

Ganjimala¹

2022

Preprint

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“…However, the binary hypothesis testing procedure inherently inflates overall Type-I error rate [13]. Recently, we employed a multiple hypothesis (MH) testing procedure to reduce the expected proportion of falsely rejected null hypotheses, i.e., false discovery rate (FDR), while estimating the VD in hyperspectral data [14]. In this article, we extend our work [14] and show its effectiveness over three broad categories of approaches, i.e., eigenanalysis-based, target specified hypothesisbased testing approach, and geometry-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Virtual Dimensionality of Hyperspectral Data: Use of Multiple Hypothesis Testing for Controlling Type-I Error

Vijayashekhar

Bhatt

Chattopadhyay

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Estimating the number of materials present in a scene is the fundamental step in many hyperspectral remote sensing applications. The virtual dimensionality (VD) estimates the number of spectrally distinct materials in the hyperspectral data. The VD is generally considered as the number of signal sources under binary hypothesis, based on the Neyman-Pearson detection criteria. We observe that the hypothesis testing procedure used in many approaches is prone to inflated Type-I (false positive) error. This is due to carrying out the binary hypothesis test individually on each band image, i.e., more than 200 images in hyperspectral data. In this article, we propose multiple hypothesis testing to control the expected proportion of falsely rejected null hypotheses, i.e., false discovery rate (FDR), and in turn, improve the probability of better performance in estimating the VD. To this end, we employ Benjamini and Hochberg procedure that controls the FDR. We provide multiple hypothesis testing-based algorithms to estimate VD wherein the hypothesis can be formulated according to eigenanalysis, the target specified by statistical approach, and by geometric analysis. The efficacies of the proposed algorithms are evaluated by estimating the number of endmembers for the spectral unmixing application. We conduct experiments on four synthetic hyperspectral data sets at different noise levels as well as on two well-known real hyperspectral datasets. Time complexity and execution time are discussed to study the algorithmic aspects while sensitivity analyses of parameters are carried out for better performance analysis of the proposed approach. We found that the use of multiple hypothesis testing improves estimation of number of endmembers in hyperspectral data. Index Terms-False discovery rate (FDR), hyperspectral remote sensing, multiple hypothesis testing, Type-I error, virtual dimensionality (VD). Vijayashekhar S S (Student Member, IEEE) received the B.E. degree in electronics and communications engineering from Visvesvaraya Technological University, Karnataka, India, in 2009, and the M.Tech. degree in digital electronics and advanced communication systems from Manipal university, Karnataka, India, in 2014. He is currently a fulltime Ph.D. Research Scholar with the

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A Statistical Approach to Improve Virtual Dimensionality of Hyperspectral Data

Cited by 2 publications

References 14 publications

Improved Real-Time Fast Simplex Growing Algorithm for Finding Endmembers in Hyperspectral Data and its FPGA architecture

Improved Real-Time Fast Simplex Growing Algorithm for Finding Endmembers in Hyperspectral Data and its FPGA architecture

Virtual Dimensionality of Hyperspectral Data: Use of Multiple Hypothesis Testing for Controlling Type-I Error

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