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

ABSTRACT

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.

KEYWORDS:

• Hyperspectral image
• Spectral-Spatial classification
• spatial features
• Probabilistic Framework
• Arithmetic Optimization Algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Islamic Azad University, Ardabil Branch, Ardabil, Iran.