Empirical Mode Decomposition of Hyperspectral Images for Support Vector Machine Classification

Demir, Begüm; Ertürk, Sarp

doi:10.1109/tgrs.2010.2070510

Cited by 99 publications

(102 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This widely-used data set contains 220 spectral bands in the range from 400 to 2500 nm, with spatial dimensions of 145×145 pixels. However, the number of spectral bands is commonly reduced from 220 to 200 to avoid some noisy bands [9,11,20]. It contains 16 labeled classes related to agriculture, forest and vegetation, although it is usual to discard 7 classes with reduced number of samples available, as we do for consistency with previous studies [8,9,11,20].…”

Section: A Data Description and Conditioningmentioning

confidence: 93%

“…However, the number of spectral bands is commonly reduced from 220 to 200 to avoid some noisy bands [9,11,20]. It contains 16 labeled classes related to agriculture, forest and vegetation, although it is usual to discard 7 classes with reduced number of samples available, as we do for consistency with previous studies [8,9,11,20]. Second, the subscene Pavia University A (Pavia UA) taken in Pavia (North Italy) is used, where the data set was captured by the Reflective Optics System Imaging Spectrometer (ROSIS) [18,21] Third, Salinas C image shown in Fig.…”

Section: A Data Description and Conditioningmentioning

confidence: 99%

“…Therefore, 2D-SSA is proven to beat in terms of classification accuracy state-of-the-art techniques in HSI, including from classic methods such as the principal component analysis (PCA) [10], median filtering and morphological operators, to more modern approaches such as the 2-D empirical mode decomposition (2D-EMD) [11], the main competitor of 2D-SSA [9]. In fact, while 2D-EMD is based on empirical iterations, becoming computationally expensive, 2D-SSA is faster, being based on the well-known singular value decomposition (SVD).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Zabalza

Qing

Yuen

et al. 2018

Journal of the Franklin Institute

View full text Add to dashboard Cite

This version is available at https://strathprints.strath.ac.uk/60870/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.

show abstract

Section: A Data Description and Conditioningmentioning

confidence: 93%

Section: A Data Description and Conditioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Zabalza

Qing

Yuen

et al. 2018

Journal of the Franklin Institute

View full text Add to dashboard Cite

show abstract

“…We also performed a classification with support vector machine SVM (Joachims 1999), which operates in implicit parameter hyperspaces by finding a manifold which divides the data of interest in two groups in the hyperspace, according to some criteria. In spite of being a general classification methodology, SVM have been often applied to hyperspectral data, due to their natural connection to multidimensional data (Demir 2010). We used a Gaussian radial basis function kernel defined as K(u, v) = exp(−γ|u − v|2), which is found to yield the best results for the classification of a different AVIRIS scene (Indian Pines) in (Melangi et al 2004).…”

Section: Methodsmentioning

confidence: 99%

Synergetics Framework for Hyperspectral Image Classification

Müller

Cerra

Reinartz

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:In this paper a new classification technique for hyperspectral data based on synergetics theory is presented. Synergetics -originally introduced by the physicist H. Haken -is an interdisciplinary theory to find general rules for pattern formation through selforganization and has been successfully applied in fields ranging from biology to ecology, chemistry, cosmology, and thermodynamics up to sociology. Although this theory describes general rules for pattern formation it was linked also to pattern recognition. Pattern recognition algorithms based on synergetics theory have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analysed independently. The classification scheme based on synergetics introduces also methods for spatial regularization to get rid of "salt and pepper" classification results and for iterative parameter tuning to optimize class weights. The paper reports an experiment on a benchmark data set frequently used for method comparisons. This data set consists of a hyperspectral scene acquired by the Airborne Visible Infrared Imaging Spectrometer AVIRIS sensor of the Jet Propulsion Laboratory acquired over the Salinas Valley in CA, USA, with 15 vegetation classes. The results are compared to state-of-the-art methodologies like Support Vector Machines (SVM), Spectral Information Divergence (SID), Neural Networks, Logistic Regression, Factor Graphs or Spectral Angle Mapper (SAM). The outcomes are promising and often outperform state-of-the-art classification methodologies.

show abstract

“…Especially for remote sensing images, 2D-EMD [14][15][16] and MEEMD [11] have been proposed recently for the decomposition of hyperspectral image into IMFs, but they apply to pre-selected two-dimensional image band instead of one-dimensional spectral information. The aim of this research is to discriminate materials by extracting the unique absorption features from the spectrum of each pixel.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data

et al. 2014

View full text Add to dashboard Cite

This study proposed a new approach to measure the similarity between spectra to discriminate materials and evaluate the performance of parameter-selection procedures. Many pure pixel vector-based similarity measurements have been developed in the past to calculate the distance between two pixel vectors. However, those methods may not be effective since they do not take full advantage of the spectral correlation. In this study, we adopt Ensemble Empirical Mode Decomposition (EEMD) to decompose the spectrum into serial components and employ these components to improve the performance of spectral discrimination. Performance evaluation was conducted with several commonly used measurements, and the spectral samples used for experimentation were provided by the spectral library of United States Geological Survey (USGS). The experimental results have demonstrated that EEMD can extract the spectral features more effectively than common spectral similarity measurements, and it better characterizes spectral properties. Our experimental results also suggest general rules for selecting noise standard deviation, the number of iterations for EEMD and the collection of Intrinsic Mode Functions (IMFs) for classification. Finally, since EEMD is a time-consuming algorithm, we also OPEN ACCESS Remote Sens. 2014, 6 2070 implement parallel processing with a Graphics Processing Unit (GPU) to increase the processing speed.

show abstract

Empirical Mode Decomposition of Hyperspectral Images for Support Vector Machine Classification

Cited by 99 publications

References 26 publications

Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Synergetics Framework for Hyperspectral Image Classification

Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data

Contact Info

Product

Resources

About