Special Issue "Maximum Entropy and Its Application III"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (31 March 2020).

Special Issue Editor

Dr. Dawn E. Holmes
Website
Guest Editor
Department of Statistics and Applied Probability, University of California, USA
Interests: Bayesian networks; machine learning; data mining; knowledge discovery; the foundations of Bayesianism
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Special Issue Information

Dear Colleagues,

The field of entropy-related research has been particularly fruitful in the past few decades and continues to produce important results in a range of scientific areas, including thermal engineering, quantum communications, and wildlife research. Contributions to this Special Issue are welcome from both the theoretical and applied perspectives of entropy, including papers addressing conceptual and methodological developments, as well as new applications of entropy and information theory. Foundational issues involving probability theory and information theory, and inference and inquiry are also of keen interest, as there are yet many open questions.

Dr. Dawn Holmes
Guest Editor

Manuscript Submission Information

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Published Papers (2 papers)

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Research

Open AccessArticle
Land-Cover Classification Using MaxEnt: Can We Trust in Model Quality Metrics for Estimating Classification Accuracy?
Entropy 2020, 22(3), 342; https://doi.org/10.3390/e22030342 - 17 Mar 2020
Abstract
MaxEnt is a popular maximum entropy-based algorithm originally developed for modelling species distribution, but increasingly used for land-cover classification. In this article, we used MaxEnt as a single-class land-cover classification and explored if recommended procedures for generating high-quality species distribution models also apply [...] Read more.
MaxEnt is a popular maximum entropy-based algorithm originally developed for modelling species distribution, but increasingly used for land-cover classification. In this article, we used MaxEnt as a single-class land-cover classification and explored if recommended procedures for generating high-quality species distribution models also apply for generating high-accuracy land-cover classification. We used remote sensing imagery and randomly selected ground-true points for four types of land covers (built, grass, deciduous, evergreen) to generate 1980 classification maps using MaxEnt. We calculated different accuracy discrimination and quality model metrics to determine if these metrics were suitable proxies for estimating the accuracy of land-cover classification outcomes. Correlation analysis between model quality metrics showed consistent patterns for the relationships between metrics, but not for all land-covers. Relationship between model quality metrics and land-cover classification accuracy were land-cover-dependent. While for built cover there was no consistent patterns of correlations for any quality metrics; for grass, evergreen and deciduous, there was a consistent association between quality metrics and classification accuracy. We recommend evaluating the accuracy of land-cover classification results by using proper discrimination accuracy coefficients (e.g., Kappa, Overall Accuracy), and not placing all the confidence in model’s quality metrics as a reliable indicator of land-cover classification results. Full article
(This article belongs to the Special Issue Maximum Entropy and Its Application III)
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Open AccessArticle
Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions
Entropy 2019, 21(9), 915; https://doi.org/10.3390/e21090915 - 19 Sep 2019
Abstract
Bello’s stochastic linear time-varying system theory has been widely used in the wireless communications literature to characterize multipath fading channel statistics. In the context of radar backscatter, this formulation allows for statistical characterization of distributed radar targets in range and Doppler using wide-sense [...] Read more.
Bello’s stochastic linear time-varying system theory has been widely used in the wireless communications literature to characterize multipath fading channel statistics. In the context of radar backscatter, this formulation allows for statistical characterization of distributed radar targets in range and Doppler using wide-sense stationary uncorrelated scattering (WSSUS) models. WSSUS models separate the channel from the effect of the waveform and receive filter, making it an ideal formulation for waveform design problems. Of particular interest in the radar waveform design community is the ability to suppress unwanted backscatter from the earth’s surface, known as clutter. Various methods for estimating WSSUS system functions have been studied in the literature, but to date no analytic expressions for radar surface clutter range-Doppler scattering functions exist. In this work we derive a frequency-selective generalization of the Jakes Doppler spectrum model, which is widely used in the wireless communications literature, adapt it for use in radar problems, and show how the maximum entropy method can be used to extend this model to account for internal clutter motion. Validation of the spectral and stationarity properties of the proposed model against a subset of the Australian Ingara sea clutter database is performed, and good agreement is shown. Full article
(This article belongs to the Special Issue Maximum Entropy and Its Application III)
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