2020
DOI: 10.1109/access.2020.2994345
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Efficient approach for computing the discrimination ratio-based variant of information entropy for image processing

Abstract: Information content is an important criterion for many image processing algorithms such as band selection and image fusion. Usually, information content is quantified by using information entropy (i.e., Shannon entropy); however, this is not a suitable measure because information entropy is independent of the spatial distribution of image pixels. Thus, improved information entropies and variants of information entropy have been developed. Among all the entropic measures, the discrimination ratio-based variant … Show more

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Cited by 17 publications
(9 citation statements)
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References 51 publications
(57 reference statements)
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“…For example, Leopold and Langbein [2] presented the "concept of entropy in landscape evolution", and proposed that landscapes evolve toward maximization of entropy, measured by the most probable condition of the landscape with regard to the distribution of matter and energy, and proposed a series of equations to measure and predict this in hydrological systems. This focus on landscape evolution and maximization of entropy has been rekindled in recent research on calculating Boltzmann configurational entropy of landscapes (e.g., [14,15,17,22]), but this new focus is best seen as the continuation of an older line of work on entropy in ecology, which formally focused on thermodynamic and structural aspects of ecosystems and landscapes.…”
Section: Discussionmentioning
confidence: 99%
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“…For example, Leopold and Langbein [2] presented the "concept of entropy in landscape evolution", and proposed that landscapes evolve toward maximization of entropy, measured by the most probable condition of the landscape with regard to the distribution of matter and energy, and proposed a series of equations to measure and predict this in hydrological systems. This focus on landscape evolution and maximization of entropy has been rekindled in recent research on calculating Boltzmann configurational entropy of landscapes (e.g., [14,15,17,22]), but this new focus is best seen as the continuation of an older line of work on entropy in ecology, which formally focused on thermodynamic and structural aspects of ecosystems and landscapes.…”
Section: Discussionmentioning
confidence: 99%
“…Cushman [7] further demonstrated the generality of this method and its practical application to large landscapes with many patch types. This inspired a number of recent papers evaluating different measures of landscape configurational entropy [16][17][18][19][20][21][22].…”
Section: Introductionmentioning
confidence: 97%
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“…Specifically, band selection preserves the most discriminative bands while discarding some redundant and noisy bands to reduce the band number [26][27][28][29][30][31][32]. The classical methods such as maximum variance principal component analysis (MVPCA) [26], constrained band selection (CBS) [27], entropy-based [28][29][30], and evolution-based band selection [31] have been shown to be effective. Recently, a Boltzmann entropy-based band selection was developed, which can characterize both statistical information of HSI and spatial distribution of pixels [29].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, a Boltzmann entropy-based band selection was developed, which can characterize both statistical information of HSI and spatial distribution of pixels [29]. Moreover, a band selection method based on a variant of entropy has also proved efficient [30]. As a limitation, the band subsets selected by most of these traditional band selection techniques are not enough to describe the discriminants for different types of classes to some extent [32].…”
Section: Introductionmentioning
confidence: 99%