Spectral-spatial classification fusion for hyperspectral images in the probabilistic framework via arithmetic optimization Algorithm

Spectral data and spatial information such as shape and texture features can be fused to improve the classification of the hyperspectral images. In this paper, a novel approach of the spectral and spatial features (texture features and shape features) fusion in the probabilistic framework is propose...

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Bibliographic Details
Published in:International journal of image and data fusion 2022-07, Vol.13 (3), p.262-277
Main Authors: Seifi Majdar, Reza, Ghassemian, Hassan
Format: Article
Language:English
Subjects:
Algorithms
Arithmetic
Arithmetic Optimization Algorithm
Classification
Gabor filters
Heuristic methods
Hyperspectral image
Hyperspectral imaging
Image classification
Mathematics
Optimization
Optimization algorithms
Parameters
Probabilistic Framework
Spatial data
spatial features
Spectral-Spatial classification
Support vector machines
Texture
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Summary:Spectral data and spatial information such as shape and texture features can be fused to improve the classification of the hyperspectral images. In this paper, a novel approach of the spectral and spatial features (texture features and shape features) fusion in the probabilistic framework is proposed. The Gabor filters are applied to obtain the texture features and the morphological profiles (MPs) are used to obtain the shape features. These features are classified separately by the support vector machine (SVM); therefore, the per-pixel probabilities can be estimated. A novel meta-heuristic optimization method called Arithmetic Optimization Algorithm (AOA) is used to weighted combinations of these probabilities. Three parameters, α, β and γ determine the weight of each feature in the combination. The optimal value of these parameters is calculated by AOA. The proposed method is evaluated on three useful hyperspectral data sets: Indian Pines, Pavia University and Salinas. The experimental results demonstrate the effectiveness of the proposed combination in hyperspectral image classification, particularly with few labelled samples. As well as, this method is more accurate than a number of new spectral-spatial classification methods.
ISSN:1947-9832
1947-9824
DOI:10.1080/19479832.2021.2001051