Benefits of textural characterization for the classification of hyperspectral images

Roussel, Guillaume; Achard, Véronique; Alakian, Alexandre; Fort, Jean‐Claude

doi:10.1109/whispers.2010.5594867

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…In the Bayesian estimation procedure, a bandwidth parameter η controls the percentage of the spectral grid used to approximate the Gaussian likelihood and is set to η = 0.3, see [13]. Scales used to perform estimation are respectively [j1, j2] = [2,4], [1,2] and [1,3] for patches of size 256 × 256, 64 × 64 and 16 × 16 pixels.…”

Section: Estimationmentioning

confidence: 99%

“…A number of studies suggests that the combination of both spectral and spatial information can improve the performance in classical HS image processing tasks, such as classification [1,2], segmentation [3] or endmember identification [4,5]. Several authors have proposed to extract spatial information by using textural characterizations, see, e.g., [2,5].…”

Section: Introductionmentioning

confidence: 99%

“…Several authors have proposed to extract spatial information by using textural characterizations, see, e.g., [2,5]. The notion of image texture does not rely on one single concept, and different paradigms have been proposed in the literature [6].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral image analysis using multifractal attributes

Combrexelle

Wendt

Tourneret

et al. 2015

2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 17041The contribution was presented at WHISPERS 2015 : http://www.ieee-whispers.com/index.php/past-editions/2015-tokyo-japan ABSTRACTThe increasing spatial resolution of hyperspectral remote sensors requires the development of new processing methods capable of combining both spectral and spatial information. In this article, we focus on the spatial component and propose the use of novel multifractal attributes, which extract spatial information in terms of the fluctuations of the local regularity of image amplitudes. The novelty of the proposed approach is twofold. First, unlike previous attempts, we study attributes that efficiently summarize multifractal information in a few parameters. Second, we make use of the most recent developments in multifractal analysis: wavelet leader multifractal formalism, the current theoretical and practical benchmark in multifractal analysis, and a novel Bayesian estimation procedure for one of the central multifractal parameters. Attributes provided by these stateof-the-art multifractal analysis procedures are studied on two sets of hyperspectral images. The experiments suggest that multifractal analysis can provide relevant spatial/textural attributes which can in turn be employed in tasks such as classification or segmentation.

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Section: Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyperspectral image analysis using multifractal attributes

Combrexelle

Wendt

Tourneret

et al. 2015

2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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“…In [21][22][23], the authors compare different spatial features in unsupervised classification of hyperspectral images; the studies used Gabor filter banks, co-occurrence matrices, Texture spectra and morphological profiles. The results obtained showed that the haralick features extracted from the cooccurrence matrices give the best performance in classification accuracies.…”

Section: B Spatial Informationmentioning

confidence: 99%

SVM Classification of Urban High-Resolution Imagery Using Composite Kernels and Contour Information

Bekkari¹,

Yassa²,

Idbraim³

et al. 2013

IJACSA

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Abstract-The classification of remote sensing images has done great forward taking into account the image's availability with different resolutions, as well as an abundance of very efficient classification algorithms. A number of works have shown promising results by the fusion of spatial and spectral information using Support Vector Machines (SVM) which are a group of supervised classification algorithms that have been recently used in the remote sensing field, however the addition of contour information to both spectral and spatial information still less explored.For this purpose we propose a methodology exploiting the properties of Mercer's kernels to construct a family of composite kernels that easily combine multi-spectral features and Haralick texture features as data source. The composite kernel that gives the best results will be used to introduce contour information in the classification process.The proposed approach was tested on common scenes of urban imagery. The three different kernels tested allow a significant improvement of the classification performances and a flexibility to balance between the spatial and spectral information in the classifier. The experimental results indicate a global accuracy value of 93.52%, the addition of contour information, described by the Fourier descriptors, Hough transform and Zernike moments, allows increasing the obtained global accuracy by 1.61% which is very promising.

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Benefits of textural characterization for the classification of hyperspectral images

Cited by 2 publications

References 7 publications

Hyperspectral image analysis using multifractal attributes

Hyperspectral image analysis using multifractal attributes

SVM Classification of Urban High-Resolution Imagery Using Composite Kernels and Contour Information

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