Efficient multi-temporal hyperspectral signatures classification using a Gaussian-Bernoulli RBM based approach

This paper presents an efficient Gaussian-Bernoulli Restricted Boltzmann Machines (GB-RBM) framework in order to better address the classification challenge of remotely sensed images. The proposed approach relies on generating well-designed features for a new 3D modality of spectral signature. For t...

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Bibliographic Details
Published in:Pattern recognition and image analysis 2016, Vol.26 (1), p.190-196
Main Authors: Hemissi, S., Farah, Imed Riadh
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Applied Problems
Classification
Computer Science
Engineering Sciences
Image analysis
Image classification
Image Processing and Computer Vision
Image processing systems
Machine learning
Noise
Pattern Recognition
Remote sensing
Signal and Image processing
Signatures
Spectral signatures
Studies
Three dimensional
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Summary:This paper presents an efficient Gaussian-Bernoulli Restricted Boltzmann Machines (GB-RBM) framework in order to better address the classification challenge of remotely sensed images. The proposed approach relies on generating well-designed features for a new 3D modality of spectral signature. For this purpose, mesh smoothing is introduced to reduce noise while conserving the main geometric features of the multi-temporal spectral signature. Then, we propose the use of an RBM (Restricted Boltzmann Machine) framework as stand-alone non-linear classifier. The adapted framework focuses on a cooperative integrated generative-discriminative objective allowing the integration of modeling input features and their classification process in one-pass algorithm. The main benefit of the proposed approach is the ability to learn more discriminative features. We evaluated our approach within different scenarios and we demonstrated its usefulness for noisy high dimensional hyperspectral images.
ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661816010211