An Icon-Based Synoptic Visualization of Fully Polarimetric Radar Data

Turner, D.; Woodhouse, Iain

doi:10.3390/rs4030648

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…5 We achieve homogenization while preserving the most important structures inside broader areas. The proposed approach could be helpful to other procedures, like the one proposed in [34], where different labels from PolSAR data analysis are assigned to groups of pixels instead of the isolated pixels.…”

Section: Discussionmentioning

confidence: 99%

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach

et al. 2012

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This paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU) times.

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Section: Discussionmentioning

confidence: 99%

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach

et al. 2012

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“…The second consisted of a short survey of high dimensional data representation techniques and some possible visualization tools. Geometric (Ankerst et al, 2011), iconbased (Turner and Woodhouse, 2012), and pixel-oriented (Lammarsch et al, 2009) methods were covered, even though many of these methods are not generally applied to acoustic data directly. The intent was to incorporate various methods to encourage novel applications.…”

Section: Course Contentmentioning

confidence: 99%

Design and outcomes of an acoustic data visualization seminar

Robinson

Pätynen

Haapaniemi

et al. 2013

The Journal of the Acoustical Society of America

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Recently, the Department of Media Technology at Aalto University offered a seminar entitled Applied Data Analysis and Visualization. The course used spatial impulse response measurements from concert halls as the context to explore high-dimensional data visualization methods. Students were encouraged to represent source and receiver positions, spatial aspects, and temporal development of sound fields, frequency characteristics, and comparisons between halls, using animations and interactive graphics. The primary learning objectives were for the students to translate their skills across disciplines and gain a working understanding of high-dimensional data visualization techniques. Accompanying files present examples of student-generated, animated and interactive visualizations.

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“…Thus, they have potential to increase further the ability of extracting physical quantities of the scattering targets. Therefore, they are used in broad fields of study such as visualization for classification [1,2], oil detection [3,4], and ship detection [5,6], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Four-Component Scattering Power Decomposition Algorithm with Rotation of Covariance Matrix Using ALOS-PALSAR Polarimetric Data

2012

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The present study introduces the four-component scattering power decomposition (4-CSPD) algorithm with rotation of covariance matrix, and presents an experimental proof of the equivalence between the 4-CSPD algorithms based on rotation of covariance matrix and coherency matrix. From a theoretical point of view, the 4-CSPD algorithms with rotation of the two matrices are identical. Although it seems obvious, no experimental evidence has yet been presented. In this paper, using polarimetric synthetic aperture radar (POLSAR) data acquired by Phased Array L-band SAR (PALSAR) on board of Advanced Land Observing Satellite (ALOS), an experimental proof is presented to show that both algorithms indeed produce identical results.

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An Icon-Based Synoptic Visualization of Fully Polarimetric Radar Data

Cited by 6 publications

References 21 publications

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach

Design and outcomes of an acoustic data visualization seminar

Four-Component Scattering Power Decomposition Algorithm with Rotation of Covariance Matrix Using ALOS-PALSAR Polarimetric Data

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