Separability indices and their use in radar signal based target recognition

Mishra, Amit Kumar

doi:10.1587/elex.6.1000

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…Finding a suitable combination for existing earth features is known as band separability analysis. The divergence measures decide the separability between the two spectral classes, say i and j. Jeffreys Matusita Distance (JM ij ), Divergence (D ij ), transform divergence (T D ij ) and Bhattacharyya distance (B ij ) are some well known measures of separability [49] [50]. Jeffreys-Matusita distance (JM ij ) for the class pair (i, j) is defined by Equation 1.…”

Section: B Band Separability Analysismentioning

confidence: 99%

Mapping Phytoplankton and Algal Blooms With a Novel Multisensor Water Index

Mishra,

Mishra

2024

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

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Multispectral Satellite Imageries (MSI) are strong enough to delineate features of interest by suppressing others. The water index is a method applied to delineate the water features by combining the multispectral band images. This research proposes a novel index for extracting water features from multi-sensor MSIs named Multi Sensor Water Index (MSWI). The proposed index, along with existing indices, is applied on Landsat-8 OLI imageries, a comparative analysis is done, and it is found that the proposed index is outperforming with 99.77% accuracy. The proposed index was validated on other sensors, such as a series of Landsat (5, 7, 8, and 9), Sentinel-2, Resourcesat, and Modis satellite imagery, by mapping water features successfully. Further, the proposed index maps marine pollution by discriminating phytoplankton and algal bloom from the water feature. It is observed that the increase in phytoplankton or algal bloom is the result of eutrophication. The blooming of phytoplankton and algae is on the surface of the water. Thus, it causes shading at the bottom of the marine ecosystem and negatively impacts the growth of seagrasses, dissolved oxygen levels, fish suffocation, water properties, and other bottom habitats. The three test sites, viz., lake Villarrica, lake Okeechobee and the Atlantic Ocean, have been considered to study increasing phytoplankton and algal bloom. The proposed index successfully discriminates the phytoplankton and algal bloom from the water features.

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Section: B Band Separability Analysismentioning

confidence: 99%

Mapping Phytoplankton and Algal Blooms With a Novel Multisensor Water Index

Mishra,

Mishra

2024

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

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“…By contrast d ′ itself as usually defined does not generalize to higher dimensions, and it is unclear what measure could replace it. The criteria for a multidimensional measure of category overlap are still debated (e.g., see Mishra, 2009). Feldman (2012) (in a very different context) suggested using the ratio of the variance of the category means to the maximum within-category variance, a variant of Fisher’s F statistic, but that measure is relatively coarse.…”

Section: Higher Dimensions and Larger Numbers Of Distributionsmentioning

confidence: 99%

Information-theoretic signal detection theory.

Feldman¹

2021

Psychological Review

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Signal detection theory (SDT), the standard mathematical framework by which we understand how stimuli are classified into distributions such as signal or noise, is an essential part of the modern psychologist's toolkit. This article introduces some mathematical tools derived from information theory which allow surprisingly simple approximations to key quantities in SDT. The main idea is a lower bound on the probability of correct classification of a stimulus, as a function of information-theoretic properties of the generating distribution. This bound depends on three distinct factors, each of which can be quantified information-theoretically: (a) The prior uncertainty in the choice of generating distribution; (b) the inherent separability of the classes; and (c) the discrepancy between the observer's model of the class distributions and the "true" model. The bound is only a loose substitute for the conventional method for computing proportion correct (via integration) but generalizes readily to multiple dimensions and larger numbers of stimulus categories, where direct integration is computationally difficult. Moreover, unlike most conventional SDT formulae, this bound does not require Gaussian distributions. Most importantly, the information-theoretic signal detection theory (IT-SDT) framework sheds light on the way classification performance depends on the discrepancy between the observer's assumptions and those actually governing the environment.

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“…As such, we use the Bhattacharyya distance, which does take into account covariance and acts as an important statistical measure of the separability of two distributions [20,21]. If we assumed that each of our three classes can be modeled as a multivariate normal distribution, we may estimate the covariance matrices and means of the distributions associated with the classes.…”

Section: Classification Using a Single Waveletmentioning

confidence: 99%

Wavelet-Based Compressive Sensing for Point Scatterers

Mishra¹,

Wilsenach²

2015

RADIOENGINEERING

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Abstract. Compressive Sensing (CS) allows for the sampling of signals at well below the Nyquist rate but does so, usually, at the cost of the suppression of lower amplitude signal components. Recent work suggests that important information essential for recognizing targets in the radar context is contained in the side-lobes as well, which are often suppressed by CS. In this paper we extend existing techniques and introduce new techniques both for improving the accuracy of CS reconstructions and for improving the separability of scenes reconstructed using CS. We investigate the Discrete Wavelet Transform (DWT), and show how the use of the DWT as a representation basis may improve the accuracy of reconstruction generally. Moreover, we introduce the concept of using multiple wavelet-based reconstructions of a scene, given only a single physical observation, to derive reconstructions that surpass even the best wavelet-based CS reconstructions. Lastly, we specifically consider the effect of the wavelet-based reconstruction on classification. This is done indirectly by comparing outputs of different algorithms using a variety of separability measures. We show that various wavelet-based CS reconstructions are substantially better than conventional CS approaches at inducing (or preserving) separability, and hence may be more useful in classification applications.

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Separability indices and their use in radar signal based target recognition

Cited by 8 publications

References 8 publications

Mapping Phytoplankton and Algal Blooms With a Novel Multisensor Water Index

Mapping Phytoplankton and Algal Blooms With a Novel Multisensor Water Index

Information-theoretic signal detection theory.

Wavelet-Based Compressive Sensing for Point Scatterers

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