<i>K</i>-means algorithm based on stochastic distances for polarimetric synthetic aperture radar image classification

Negri, Rogério Galante; Silva, Wagner Barreto da; Mendes, Tatiana Sussel Gonçalves

doi:10.1117/1.jrs.10.045005

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…From the enterprise level, the creative ability of Chinese enterprises is not high, and the subject status is difficult to establish. The innovation power is insufficient, and the institutional foundation and social and cultural foundation are still relatively weak [ 1 ]. There is a shortage of external and internal investment resources for innovation, and a market-oriented enterprise's independent innovation mechanism has yet to be formed.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Motivation Mechanism of the Formation of Corporate Health Strategic Innovation Capability Based on the K-Means Algorithm

Shang

2022

Computational Intelligence and Neuroscience

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Improving enterprises’ independent innovation capability is critical to improving their competitive strength, industries’ independent innovation capability, industries’ international competitiveness, and countries’ independent innovation capability, as well as building an innovative country. It is now the era of strategic innovation. Many growing businesses are focused on strategic innovation, but they overlook the issue of ensuring that strategic innovation is implemented. Management and innovation are perennial themes in the long-term development of businesses. The most pressing issue in enterprises’ strategic innovation activities is how to combine their strategic direction with their superior ability and choose a strategic innovation path that is appropriate for their development. This paper uses data mining theory to establish the K-means clustering algorithm to identify the best strategic orientation of enterprise innovation strategic direction selection based on the existing advantages and capabilities of enterprises based on the analysis of the direction of enterprise strategic innovation.

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Section: Introductionmentioning

confidence: 99%

An Analysis of the Motivation Mechanism of the Formation of Corporate Health Strategic Innovation Capability Based on the K-Means Algorithm

Shang

2022

Computational Intelligence and Neuroscience

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“…Adopting stochastic distance between Complex Multivariate Wishart models and a hypothesis test derived from this kind of measure, Negri, Silva, and Mendes (2016) presented a new version of K-Means algorithm for region-based classification of PolSAR data. Similarly, verified the use of Bhattacharyya distance with a Support Vector Machine (SVM) through kernel functions for region-based classification of SAR data.…”

Section: Introductionmentioning

confidence: 99%

Region-based classification of PolSAR data using radial basis kernel functions with stochastic distances

Negri¹,

Frery²,

Silva³

et al. 2021

Preprint

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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. Region-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and segments. Silva et al. [“Classification of segments in PolSAR imagery by minimum stochastic distances between wishart distributions.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (3): 1263–1273] used stochastic distances between complex multivariate Wishart models which, differently from other measures, are computationally tractable. In this work we assess the robustness of such approach with respect to errors in the training stage, and propose an extension that alleviates such problems. We introduce robustness in the process by incorporating a combination of radial basis kernel functions and stochastic distances with Support Vector Machines (SVM). We consider several stochastic distances between Wishart: Bhatacharyya, Kullback-Leibler, Chi-Square, Rényi, and Hellinger. We perform two case studies with PolSAR images, both simulated and from actual sensors, and different classification scenarios to compare the performance of Minimum Distance and SVM classification frameworks. With this, we model the situation of imperfect training samples. We show that SVM with the proposed kernel functions achieves better performance with respect to Minimum Distance, at the expense of more computational resources and the need of parameter tuning. Code and data are provided for reproducibility.

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A fast SAR image segmentation method based on improved chicken swarm optimization algorithm

Liang

Wang

et al. 2018

Multimed Tools Appl

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K-means algorithm based on stochastic distances for polarimetric synthetic aperture radar image classification

Cited by 7 publications

References 24 publications

An Analysis of the Motivation Mechanism of the Formation of Corporate Health Strategic Innovation Capability Based on the K-Means Algorithm

An Analysis of the Motivation Mechanism of the Formation of Corporate Health Strategic Innovation Capability Based on the K-Means Algorithm

Region-based classification of PolSAR data using radial basis kernel functions with stochastic distances

A fast SAR image segmentation method based on improved chicken swarm optimization algorithm

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