The contribution of the sources separation method in the decomposition of mixed pixels

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

doi:10.1109/tgrs.2004.834764

Cited by 26 publications

(14 citation statements)

References 10 publications

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“…Therefore, there is a decrease in the rate of correlation between the source images. At this level, the decorrelation is achieved in the spatial domain [14]- [9]. A visual analysis shows the important contribution of the source separation method to discriminate natural themes compared to original images " Fig.…”

Section: B Source Separation Principlementioning

confidence: 99%

Blind source separation of hyperspectral images in DCT-domain

Karray

Loghmari

Naceur

2010

2010 5th Advanced Satellite Multimedia Systems Conference and the 11th Signal Processing for Space Communications Workshop

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In this paper, we consider the problem of blind image separation by taking advantage of the sparse representation of the study images in the DCT-domain. Blind source separation (BSS) is an important field of research in signal and image processing. The BSS problem has been considered either directly in the original domain of observations or in a transform domain. The idea behind transform domains is that usually an invertible linear transform restructures the signal/image values to give transform coefficients more easily to separate. This paper describes a new method for blind source separation. The latter takes advantage of the sparse representation of structured data in large overcomplete dictionaries to separate independent features. Furthermore, DCT exhibits excellent energy compaction for highly correlated images such as hyperspectral images, which permits to reduce significantly the complexity of the separation. For this purpose, we will exploit the redundancy of neighboring pixels and the correlation of adjacent bands by a new source separation approach based jointly on the Blind Source Separation (BSS) and Discrete Cosine Transform (DCT). In this work, we differentiate from the previous works by using a second order source separation criterion in the frequency domain. The extracted independent components may lead to a meaningful data representation which permits to extract information at a finer level of precision. This approach is of utmost importance in the classification process and should minimize the misclassification risk of hyperspectral images.

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Section: B Source Separation Principlementioning

confidence: 99%

Blind source separation of hyperspectral images in DCT-domain

Karray

Loghmari

Naceur

2010

2010 5th Advanced Satellite Multimedia Systems Conference and the 11th Signal Processing for Space Communications Workshop

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“…In the H-resolution case, the Earth's surface varies at a lower spatial frequency than image sampling and features can be resolved. L-resolution is the cause of a common problem in identifying features from remotely sensed imagery, that of mixed pixels, so called because they represent areas comprising a mixture of two or more features, such as land-cover types (Chhikara 1984, Cracknell 1998, Naceur et al 2004. This can result in error where pure land-cover types are the focus of analysis, as is usually the case.…”

Section: Spatial Variabilitymentioning

confidence: 99%

On scales and dynamics in observing the environment

Aplin

2006

International Journal of Remote Sensing

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Natural and anthropogenic processes at the Earth's surface operate at a range of spatial and temporal scales. Different scales of observation are required to match the spatial scales of the processes under observation. At the same time, the temporal sampling rate of the observing systems must be reconciled with the dynamics of the processes observed. Bringing together these issues requires insight, innovation and, inevitably, compromise. This paper reviews spatial and temporal considerations in remote sensing and introduces the papers in this Special Issue on 'Scales and Dynamics in Observing the Environment'. The review comprises three main sections. The first section focuses on spatial variability in remote sensing, while the second section focuses on temporal variability in remote sensing. The third section links these two issues, focusing on the interplay of space and time in remote sensing. The review is primarily theoretical, explaining spatial and temporal properties of remote sensing and remotely sensed phenomena. Where appropriate, however, practical examples are included to demonstrate how remote sensing is used in environmental applications. Following the review, the papers included in the Special Issue are introduced, outlining their significance in the context of 'Scales and Dynamics in Observing the Environment'.

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“…In remote sensing, BSS is mainly used for unsupervised classification of rock, vegetation and urban areas (e.g. Lennon et al, 2001a, 2001b, Naceur et al, 2004and Nascimento and Dias, 2004. It is less frequently used for identification of natural materials present in mixed pixels (Gomez et al, 2007).…”

Section: Introductionmentioning

confidence: 99%