A Spectral and Spatial Source Separation of Multispectral Images

Loghmari, Mohamed Anis; Naceur, Mohamed Saber; Boussema, Mohamed Rached

doi:10.1109/tgrs.2006.882261

Cited by 21 publications

(17 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Para este caso, se utilizó el software ENVI, el cual ofrece una inter-IDFH JUi¿FD GH XVXDULR *8, SDUD SURSRUFLRQDU acceso a las funciones de procesamiento de imá-genes satelitales [8].…”

Section: Comparacion De Resultadosunclassified

Métodos de segmentación de nubes en imágenes satelitales

Vargas

Neira

Arango³

2013

View full text Add to dashboard Cite

El presente artículo tiene como objetivo mostrar los resultados de la aplicación de dos técnicas de segmentación de nubes en imágenes satelitales GOES; la primera, es una técnica basada en regiones, la umbralización por niveles de grises; y la segunda, es una técnica basada en fronteras la Transformada Hough. Finalmente, se comparan los resultados encontrados por los dos métodos con la segmentación obtenida a partir de un software especializado en imágenes satelitales mediante la separación de la banda espectral con la información de interés.

show abstract

“…Para este caso, se utilizó el software ENVI, el cual ofrece una inter-IDFH JUi¿FD GH XVXDULR *8, SDUD SURSRUFLRQDU acceso a las funciones de procesamiento de imá-genes satelitales [8].…”

Section: Comparacion De Resultadosunclassified

Métodos de segmentación de nubes en imágenes satelitales

Vargas

Neira

Arango³

2013

View full text Add to dashboard Cite

show abstract

“…Hyper-spectral imaging has been employed in various scientific disciplines to provide valuable data for fields such as astronomy [1], earth sciences and remote sensing [2,3], food safety [4], criminal investigation, and computer vision [5]. With the advances in technology and cost reductions, the hyperspectral imaging of historical art and murals is now accessible for use at mural protection and research institutes.…”

Section: Introductionmentioning

confidence: 99%

Extracting graphite sketch of the mural using Hyper-Spectral Imaging method

et al. 2015

View full text Add to dashboard Cite

Original scientific paper Many contaminants appear in murals as time passes, which make the original mural blurred and difficult to recognize; therefore, extracting a clear graphite sketch of the mural is significant. In this study, we used invisible spectra, particularly near-infrared (NIR) bands, to detect the graphite information and strengthen the features of the mural information to obtain a graphite sketch. This is the first study to extract the contour line from the draft of the mural using hyper-spectral imaging (HSI) technology. First, spectral matching methods were used to identify the pigment of the contour line and graphite was determined as the main pigment of the draft. Then, the characteristic bands were selected by analysing the spectra of the pigments. After that, the information extraction method was used to extract the graphite information. The results showed that the method could improve the efficiency of graphite information extraction significantly. The key steps of the current method involved extracting the graphite contour line end-member spectrum, followed by mapping the grey image of the graphite contour line spectrum. Finally, the visually enhanced image was reconstructed using the alpha blending fusion method with the original visible image and the graphite information image. The efficiency of results is evaluated by quantitative methods. The study also explained and discussed the two key points of election thresholds in obtaining the graphite sketch. These results demonstrate that the method is efficient for extracting graphite sketch based on hyper-spectral data of mural, and that it could provide useful information to explore cultural relics and to support some other protection researches. Keywords: graphite contour line; Hyper-Spectral Imaging; information extraction; murals; visual enhancement Dobivanje grafitne skice murala primjenom metode Hyper-Spectral ImagingIzvorni znanstveni rad Kako vrijeme prolazi murali postaju sve zagađeniji te originalni mural postaje nejasan i teško prepoznatljiv; stoga je važno dobivanje jasne grafitne skice murala. U ovom smo radu primijenili nevidljive spektre, posebice skoro-infracrvene pojaseve (near-infrared (NIR) bands), kako bismo otkrili podatke o grafitu i učvrstili karakteristične informacije o muralu za dobivanje grafitne skice. Ovo je prvo istraživanje u svrhu dobivanja osnovne linije konture iz skice murala primjenom tehnologije hiper-spektralnog slikovnog prikaza (hyper-spectral imaging (HSI). Najprije se metodama spektralne kompatibilnosti identificirao pigment osnovne konture te se grafit odredio kao osnovni pigment skice. Tada su se odabrali karakteristični pojasevi analizom spektra pigmenata. Nakon toga, metodom izvlačenja informacija dobivena su saznanja o grafitu. Rezultati su pokazali da se tom metodom mogla značajno povećati učinkovitost dobivanja novih spoznaja o grafitu. Ključni postupci ove metode obuhvatili su dobivanje spektra završnog elementa osnovne konture grafita, te mapiranje sive slike spektra osnovne konture grafit...

show abstract

“…In this framework, many interesting works within the source separation process and pattern classification axes have arisen (Dobigeon and al., 2009) (Halimi and al., 2011).The first axe, is the mixing model relaying observations to land cover. The phenomenon is non linear due to the radiance emitted by heterogeneous soil and by reflections over atmosphere layers and clouds (Loghmari and al., 2006). Many existent source separation models are based on various assumptions and simplifications.…”

Section: Introductionmentioning

confidence: 99%

Two Levels Fusion Decision for Multispectral Image Pattern Recognition

Elmannai

Loghmari

Naceur

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Major goal of multispectral data analysis is land cover classification and related applications. The dimension drawback leads to a small ratio of the remote sensing training data compared to the number of features. Therefore robust methods should be associated to overcome the dimensionality curse. The presented work proposed a pattern recognition approach. Source separation, feature extraction and decisional fusion are the main stages to establish an automatic pattern recognizer. The first stage is pre-processing and is based on non linear source separation. The mixing process is considered non linear with gaussians distributions. The second stage performs feature extraction for Gabor, Wavelet and Curvelet transform. Feature information presentation provides an efficient information description for machine vision projects. The third stage is a decisional fusion performed in two steps. The first step assign the best feature to each source/pattern using the accuracy matrix obtained from the learning data set. The second step is a source majority vote. Classification is performed by Support Vector Machine. Experimentation results show that the proposed fusion method enhances the classification accuracy and provide powerful tool for pattern recognition.

show abstract

A Spectral and Spatial Source Separation of Multispectral Images

Cited by 21 publications

References 21 publications

Métodos de segmentación de nubes en imágenes satelitales

Métodos de segmentación de nubes en imágenes satelitales

Extracting graphite sketch of the mural using Hyper-Spectral Imaging method

Two Levels Fusion Decision for Multispectral Image Pattern Recognition

Contact Info

Product

Resources

About