Contribution of the Methodologies Development for the Analysis of Remote Sensing Data

In this paper, we consider the problem of Blind source separation (BSS) method by taking advantage of the sparse modeling of the hyperspectral images. These images are produced by sensors which provide hundreds of narrow and adjacent spectral bands. The idea behind transform domains is to apply some...

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Main Authors: Karray, E., Loghmari, M. A., Elmannai, H., Naceur, M. S.
Format: Conference Proceeding
Language:English
Subjects:
Data mining
Discrete cosine transforms
Frequency domain analysis
Hyperspectral imaging
Merging
Source separation
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Loghmari, M. A.
Elmannai, H.
Naceur, M. S.
description In this paper, we consider the problem of Blind source separation (BSS) method by taking advantage of the sparse modeling of the hyperspectral images. These images are produced by sensors which provide hundreds of narrow and adjacent spectral bands. The idea behind transform domains is to apply some transformations to illustrate the dataset with a minimum of components and a maximum of essential information. To take advantages from the new representation of hyperspectral data, a novel classification approach based on using Binary Partition Trees (BPT). The BPT is obtained by iteratively merging regions and provided a combined and hierarchical representation of the image in a tree structure of regions.
doi_str_mv 10.1109/RSETE.2012.6260520
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Data mining
Discrete cosine transforms
Frequency domain analysis
Hyperspectral imaging
Merging
Source separation
title Contribution of the Methodologies Development for the Analysis of Remote Sensing Data
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