Fully Polarimetric Land Cover Classification Based on Markov Chains

Κούκιου, Γεωργία; Anastassopoulos, V.

doi:10.4236/ars.2021.103003

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…G. Koukiou and V. Anastassopoulos utilized Markov chains to categorize different types of land cover. They employed Cameron's decomposition to extract relevant features for this classification [45]. Furthermore, K. Karachristos et al conducted a study involving the use of hidden Markov models in combination with Cameron's scattering technique for supervised land cover classification [46].…”

Section: Cameron Target Decompositionmentioning

confidence: 99%

A Review on PolSAR Decompositions for Feature Extraction

Karachristos,

Koukiou,

Anastassopoulos

2024

J. Imaging

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Feature extraction plays a pivotal role in processing remote sensing datasets, especially in the realm of fully polarimetric data. This review investigates a variety of polarimetric decomposition techniques aimed at extracting comprehensive information from polarimetric imagery. These techniques are categorized as coherent and non-coherent methods, depending on their assumptions about the distribution of information among polarimetric cells. The review explores well-established and innovative approaches in polarimetric decomposition within both categories. It begins with a thorough examination of the foundational Pauli decomposition, a key algorithm in this field. Within the coherent category, the Cameron target decomposition is extensively explored, shedding light on its underlying principles. Transitioning to the non-coherent domain, the review investigates the Freeman–Durden decomposition and its extension, the Yamaguchi’s approach. Additionally, the widely recognized eigenvector–eigenvalue decomposition introduced by Cloude and Pottier is scrutinized. Furthermore, each method undergoes experimental testing on the benchmark dataset of the broader Vancouver area, offering a robust analysis of their efficacy. The primary objective of this review is to systematically present well-established polarimetric decomposition algorithms, elucidating the underlying mathematical foundations of each. The aim is to facilitate a profound understanding of these approaches, coupled with insights into potential combinations for diverse applications.

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Section: Cameron Target Decompositionmentioning

confidence: 99%

A Review on PolSAR Decompositions for Feature Extraction

Karachristos,

Koukiou,

Anastassopoulos

2024

J. Imaging

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“…Drawing from recent advancements in land cover classification using Markov chains (Koukiou & Anastassopoulos, 2021), this paper introduces a novel method for contract classification under IFRS17, tailored for motor insurance policies with the Bonus-Malus System. By leveraging Markov chains, known for capturing dynamic transitions, our approach aims to enhance classification robustness in insurance risk assessment.…”

Section: Introduction 1evolution Of Accounting Standards: Transitioni...mentioning

confidence: 99%

“…Inspired by transition matrices in land cover classification (Koukiou & Anastassopoulos, 2021), our method preserves stochastic elements in the Bonus-Malus System. This addresses subjectivity in profitability criteria, affected by claim volatility.…”

Section: Introduction 1evolution Of Accounting Standards: Transitioni...mentioning

confidence: 99%

Aggregation and Establishment of Onerous Contracts as Required by IFRS17 Based on a Stochastic Matrix and the Lumpability Concept of a Markov Chain

Zimbidis,

Gkounopoulos

2024

JFRM

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This paper aims to formulate a new innovative method for contract classification under IFRS17 for motor insurance operating under the typical Bonus-Malus System. The specific method of classification provides robust results with respect to borderlines drawn between the three categories imposed by IFRS17, as the criterion of lumpability preserves the important stochastic elements of the typical Bonus-Malus System. This approach eliminated any subjectivity which may arise when considering only the profitability criterion based on loss ratio which is affected by the high volatility of the claim experience. We apply this method to various forms of indicative stochastic matrices. Our results reveal the impact of the proposed method on contract classification. This provides valuable insights for practitioners and regulators seeking to enhance and empower their professional actuarial judgement on financial reporting and risk assessment within the IFRS17 framework.

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“…In [16] a twofold approach was presented for elementary scattering mechanisms, based on Cameron CTD and applied for automatic ship scatterers characterization. In [17] the elementary scatterers obtained from Cameron CTD are used as the states of Markov chain models to characterize polarimetric SAR land cover. More than 20 polarimetric decompositions techniques, without including Cameron's, were used in [18] to extract a set of polarimetric scattering features, which was optimized in order to improve land cover classification.…”

Section: Introductionmentioning

confidence: 99%

PolSAR Cell Information Representation by a Pair of Elementary Scatterers

Karachristos¹,

Κούκιου²,

Anastassopoulos³

2022

Remote Sensing

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This study exploits Cameron’s decomposition for polarimetric data analysis and presents an information extraction process so that each PolSAR cell (pixel) is interpreted by two dominating elementary scattering mechanisms each one contributing to the scattering behavior of the SAR pixel with its own weight. The co-distance matrix is introduced to depict the metric distances between these two nearest scattering mechanisms. For most of the scattering mechanisms in each resolution cell, the strength between the first and the second nearest elementary scatterer usually differs slightly. This indicates that the interpretation of the available information in a PolSAR pixel by a single dominant scatterer, as most methods employ, is not adequate. The proposed method presents an alternative to Cameron’s spherical topology by taking advantage of the elementary scattering mechanisms complementary nature and inspired by the philosophy of principal component analysis. According to the introduced topology four elementary scatterers, which are in pairs complementary to each other, are adequate to characterize each PolSAR pixel. The aim of this research is to present a new feature-toοl with a more stochastic nature that could fit a variety of techniques that utilize fully polarimetric data. To prove the strength of the proposed method, the double-scatterer model is applied for interpreting each pixel on a variety of land cover types presenting a richer feature extraction capability, effective in detection and classification procedures.

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Fully Polarimetric Land Cover Classification Based on Markov Chains

Cited by 10 publications

References 23 publications

A Review on PolSAR Decompositions for Feature Extraction

A Review on PolSAR Decompositions for Feature Extraction

Aggregation and Establishment of Onerous Contracts as Required by IFRS17 Based on a Stochastic Matrix and the Lumpability Concept of a Markov Chain

PolSAR Cell Information Representation by a Pair of Elementary Scatterers

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