Joint Dimensionality Reduction, Classification and Segmentation of Hyperspectral Images

Bali, Nadia; Mohammad-Djafari, Ali; Mohammadpoor, Adel

doi:10.1109/icip.2006.312649

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…In this paper we are not going to detail these methods. However, we refer here to the application of these models in different area of signal and image processing and in particular in BSS [4,5].…”

Section: Bayesian Estimators and Computational Methodsmentioning

confidence: 99%

Non Gaussianity and Non Stationarity modeled through Hidden Variables and their use in ICA and Blind Source Separation

Mohammad‐Djafari¹

2007

Preprint

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Modeling non Gaussian and non stationary signals and images has always been one of the most important part of signal and image processing methods. In this paper, first we propose a few new models, all based on using hidden variables for modeling either stationary but non Gaussian or Gaussian but non stationary or non Gaussian and non stationary signals and images. Then, we will see how to use these models in independent component analysis (ICA) or blind source separation (BSS). The computational aspects of the Bayesian estimation framework associated with these prior models are also discussed.

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Section: Bayesian Estimators and Computational Methodsmentioning

confidence: 99%

Non Gaussianity and Non Stationarity modeled through Hidden Variables and their use in ICA and Blind Source Separation

Mohammad‐Djafari¹

2007

Preprint

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“…Here, the additional difficulty is that we also have to estimate the mixing matrix A. For more details on this model and to see some typical result in joint segmentation and separation of images see [28,32,33,34,35,36].…”

Section: Joint Segmentation and Separation Of Instantaneous Mixed Imagesmentioning

confidence: 99%

Inverse problems in imaging systems and the general Bayesian inversion frawework

Mohammad‐Djafari¹

2007

Preprint

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In this paper, first a great number of inverse problems which arise in instrumentation, in computer imaging systems and in computer vision are presented. Then a common general forward modeling for them is given and the corresponding inversion problem is presented. Then, after showing the inadequacy of the classical analytical and least square methods for these ill posed inverse problems, a Bayesian estimation framework is presented which can handle, in a coherent way, all these problems. One of the main steps, in Bayesian inversion framework is the prior modeling of the unknowns. For this reason, a great number of such models and in particular the compound hidden Markov models are presented. Then, the main computational tools of the Bayesian estimation are briefly presented. Finally, some particular cases are studied in detail and new results are presented.

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“…Thus, it is usually better to eliminate those noisy spectral bands. To reduce the number of unnecessary spectral bands, a number of methods have been previously proposed [2][3][4][5]. In general, the goal of segmentation techniques is to subdivide a given image into its constituent regions.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised segmentation of hyperspectral images

Lee

2008

SPIE Proceedings

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In this paper, we propose a new unsupervised segmentation method for hyperspectral images using edge fusion. We first remove noisy spectral band images by examining the correlations between the spectral bands. Then, the Canny algorithm is applied to the retained images. This procedure produces a number of edge images. To combine these edge images, we compute an average edge image and then apply a thresholding operation to obtain a binary edge image. By applying dilation and region filling procedures to the binary edge image, we finally obtain a segmented image. Experimental results show that the proposed algorithm produced satisfactory segmentation results without requiring user input.

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Joint Dimensionality Reduction, Classification and Segmentation of Hyperspectral Images

Cited by 4 publications

References 7 publications

Non Gaussianity and Non Stationarity modeled through Hidden Variables and their use in ICA and Blind Source Separation

Non Gaussianity and Non Stationarity modeled through Hidden Variables and their use in ICA and Blind Source Separation

Inverse problems in imaging systems and the general Bayesian inversion frawework

Unsupervised segmentation of hyperspectral images

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