Non-parametric and unsupervised Bayesian classification with Bootstrap sampling

Zribi, Mourad

doi:10.1016/s0262-8856(03)00136-7

Cited by 14 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• In the region technique, segmentation using the BEM algorithm (see Section 2.1) is applied. First, the optimal size of the bootstrap sample is determined and then this sample is drawn using the replacement of a representative bootstrap sample from the totality of the image (Delleji, Zribi, and Hamida 2007;Zribi 2004). Therefore, the k-means clustering method was used to initialize the number of classes and their parameters for the input image (Kehtarnavaz and Nakamura 1998;Masson and Pieczynski 1993).…”

Section: Computation Process Of Correlation Mapmentioning

confidence: 99%

Multispectral image adaptive pansharpening based on wavelet transformation and NMDB approaches

Delleji

Kallel

Hamida

2014

International Journal of Remote Sensing

View full text Add to dashboard Cite

In remote sensing, satellite images acquired from sensors provide either high spectral or high spatial resolution. The pansharpening framework is applied to remote-sensing systems to enhance the spatial quality of coarse-resolution multispectral (MS) images using information from panchromatic imagery. A multidecomposition pansharpening approach combining MS and panchromatic (PAN) images is proposed in this paper in order to bring the resolution of the low-resolution MS imagery up to that of the panchromatic images. In particular, multilevel wavelet decomposition is applied to the luminance-chrominance (YUV) space transformation (taking into account the red green and blue (RGB) bands) or extended-YUV transformation (taking into account the near infrared (NIR) band in addition to RGB) of the original MS channels, where geometrical details from the panchromatic image are introduced into the MS ones. Our approach contains a preprocessing step that consists of homogenizing the luminance, Y, and the panchromatic image reflectance, which are, respectively, a value integrated over a wavelength spectrum and simply a linear combination of some values in the same spectrum. Hence, as the panchromatic image reflectance and luminance reflectance correspond to different measurements, they do not correspond to the same physical information, which results in a difference between their histograms. Therefore, simple histogram matching is traditionally applied to panchromatic data to fit it to the luminance to avoid colour distortion after fusion. However, as the transformation concerns just the details of the panchromatic and MS images, a new scheme for matching the images which ignores the divergence between their approximations and maximizes the resemblance between their details is proposed in this work. After that, the fusion approach is applied, and in contrast to the original approach where the details of the fused MS luminance are set equal to the PAN luminance, we propose an adaptive approach in which just a part of the PAN details proportional to the similarity between the luminance and lowered PAN image is taken. Indeed, high-resolution geometrical details cannot be similar if the low-resolution details are not in good agreement. Besides, as the agreement between PAN and MS images depends on the occupation class, we have created a segmentation map and then computed separately the correlation in each region. Finally, the evaluation is done based on QuickBird and Pleiades-1A data sets showing rural and suburban areas. When compared to recent methods, our approach provides better results.

show abstract

Section: Computation Process Of Correlation Mapmentioning

confidence: 99%

Multispectral image adaptive pansharpening based on wavelet transformation and NMDB approaches

Delleji

Kallel

Hamida

2014

International Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…A large variety of different segmentation algorithms have been developed [35][36][37]. An unsupervised Bayesian segmentation based on Bootstrapping NEM algorithm was proposed in our previous research [23]. These region characteristics favor the NEM fusion algorithm performed on each region in the next step.…”

Section: Unsupervised Bayesian Segmentation Image By Bootstrapping Nementioning

confidence: 99%

“…Thus, the parameters' estimation would be made with only the use of a small number of representative samples instead of all correlated pixels in the real image. Zribi [23] introduced the same approach to the non-parametric Expectation-Maximization (NEM) algorithm [24]. The non-parametric aspect comes from the use of the orthogonal probability density function (pdf) estimation, which could be simply reduced to the estimation of the first Fourier Coefficients (FC's) of the pdf with respect to a given orthogonal basis [25,26].…”

Section: Introductionmentioning

confidence: 99%

Non-parametric and region-based image fusion with Bootstrap sampling

Zribi

2010

Information Fusion

Self Cite

View full text Add to dashboard Cite

“…Many researchers have applied the Bootstrap approach in pattern classification [16,18,23] and in image segmentation [15]. In this section, we present the application of BSEM algorithm firstly for unsupervised image segmentation, and secondly for image fusion process.…”

Section: Bsem Algorithm Applicationmentioning

confidence: 99%

“…In fact, we tried to combine the Bootstrap approach [16][17][18] with the Stochastic EM (SEM) algorithm [19,20].…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Multi-Sensor Image Fusion Based on Bootstrap Approach and SEM Algorithm

Delleji¹,

Zribi²,

Hamida³

2009

TORMSJ

Self Cite

View full text Add to dashboard Cite

Bootstrap approach and Stochastic EM algorithm combination applied for the improvement of the multisource and multi-sensor image fusion process; was presented in this research. Improvement concerned not only image quality and reducing processing execution time as mentioned in our previous Bootstrap EM algorithm (BEM), but also regarding initialization dependence as well as fixed classes' number. Such interesting fusion algorithm for multisource and multisensor image using one stochastic phase, i.e. SEM algorithm, preceded by Bootstrap procedure was successfully implemented and tested for several prototype images. Targeted images were firstly split by an unsupervised Bayesian segmentation approach in order to determine a joint region map for the fused image. The Bootstrap approach was then applied to the targeted multisource image in conjunction with the SEM algorithm, forming hence one Bootstrap SEM algorithm called BSEM. The procedure of such algorithm involved both statistical parameters' estimation from one representative Bootstrap sample of each source or sensor images.Keywords: Multi-source multi-sensor image fusion, unsupervised bayesian segmentation, bootstrap approach, sem algorithm.

show abstract

Non-parametric and unsupervised Bayesian classification with Bootstrap sampling

Cited by 14 publications

References 26 publications

Multispectral image adaptive pansharpening based on wavelet transformation and NMDB approaches

Multispectral image adaptive pansharpening based on wavelet transformation and NMDB approaches

Non-parametric and region-based image fusion with Bootstrap sampling

Multi-Source Multi-Sensor Image Fusion Based on Bootstrap Approach and SEM Algorithm

Contact Info

Product

Resources

About