A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

Pahlavani, Parham; Bigdeli, Behnaz

doi:10.1007/s11707-016-0611-2

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…Mutual information [35][36][37] is a nonparametric and nonlinear measure index in information theory that can quantitatively express the correlation between two random variables and is more accurate than the correlation coefficient method [38]. According to the principle of irrelevance and orthogonality equivalence between zero mean random signals, mutual information can measure the coupling degree between Intrinsic Mode Function (IMF) components and residual information obtained in VMD decomposition.…”

Section: Least Squares Mutual Informationmentioning

confidence: 99%

Empirical Variational Mode Decomposition Based on Binary Tree Algorithm

Zhou

et al. 2022

Sensors

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Aiming at non-stationary signals with complex components, the performance of a variational mode decomposition (VMD) algorithm is seriously affected by the key parameters such as the number of modes K, the quadratic penalty parameter α and the update step τ. In order to solve this problem, an adaptive empirical variational mode decomposition (EVMD) method based on a binary tree model is proposed in this paper, which can not only effectively solve the problem of VMD parameter selection, but also effectively reduce the computational complexity of searching the optimal VMD parameters using intelligent optimization algorithm. Firstly, the signal noise ratio (SNR) and refined composite multi-scale dispersion entropy (RCMDE) of the decomposed signal are calculated. The RCMDE is used as the setting basis of the α, and the SNR is used as the parameter value of the τ. Then, the signal is decomposed into two components based on the binary tree mode. Before decomposing, the α and τ need to be reset according to the SNR and MDE of the new signal. Finally, the cycle iteration termination condition composed of the least squares mutual information and reconstruction error of the components determines whether to continue the decomposition. The components with large least squares mutual information (LSMI) are combined, and the LSMI threshold is set as 0.8. The simulation and experimental results indicate that the proposed empirical VMD algorithm can decompose the non-stationary signals adaptively, with lower complexity, which is O(n2), good decomposition effect and strong robustness.

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Section: Least Squares Mutual Informationmentioning

confidence: 99%

Empirical Variational Mode Decomposition Based on Binary Tree Algorithm

Zhou

et al. 2022

Sensors

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“…Despite its importance and the wide range of current and potential applications it encompasses, hyperspectral imagery exploitation is still a big challenge due to difficulty in analyzing image datasets, which can be very large in both the spatial and spectral dimensions. 11,12 *Address all correspondence to Kacem Chehdi, E-mail: kacem.chehdi@univ-rennes1.fr…”

Section: Introductionmentioning

confidence: 99%

Learning or assessment of classification algorithms relying on biased ground truth data: what interest?

Chehdi

Cariou

2019

J. Appl. Rem. Sens.

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The use of ground truth (GT) data in the learning and/or assessment of classification algorithms is essential. Using a biased or simplified GT attached to a remote sensing image to partition does not allow a rigorous explanation of the physical phenomena reflected by such images. Unfortunately, this scientific problem is not always treated carefully and is generally neglected in the relevant literature. Furthermore, the impacts of obtained classification results for decision-making are negative. This is inconsistent when considering investments in both the development of sophisticated sensors and the design of objective classification algorithms. Any GT must be validated according to a rigorous protocol before utilization, which is unfortunately not always the case. The evidence of this problem is provided, using two popular hyperspectral images (Indian Pine and Pavia University) that misleadingly are frequently used without care by the remote sensing community since the associated GTs are not accurate. The heterogeneity of the spectral signatures of some GT classes was proven using a semisupervised and an unsupervised classification method. Through this critical analysis, we propose a general framework for a minimum objective assessment and validation of the GT accuracy, before exploiting them in a classification method. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…24 Furthermore, the advances in thermal imaging technology have made it possible to be collected in multiple continuous spectral channels with significantly improved joint spectral-spatial resolutions for identification of various physical materials regardless of illumination conditions, leading thereby to enhanced classification performance. 24,29,30 The development of spectral-based classification of thermal remote sensing data has focused on spectral absorption descriptors of silicate minerals, the main parts of the terrestrial surface, and man-made construction objects. The silicon-oxygen bonds of the silicate minerals cannot present distinct spectral descriptors in the visible-to-shortwave infrared range, 31 whereas its stretching vibrations display considerable spectral features in the longwave infrared range.…”

Section: Introductionmentioning

confidence: 99%

Deep learning decision fusion for the classification of urban remote sensing data

Abdi

Samadzadegan

Reinartz³

2018

J. Appl. Rem. Sens.

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Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

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A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

Cited by 9 publications

References 21 publications

Empirical Variational Mode Decomposition Based on Binary Tree Algorithm

Empirical Variational Mode Decomposition Based on Binary Tree Algorithm

Learning or assessment of classification algorithms relying on biased ground truth data: what interest?

Deep learning decision fusion for the classification of urban remote sensing data

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