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

2021 ◽  
pp. 1-16
Author(s):  
Reza Seifi Majdar ◽  
Hassan Ghassemian
Keyword(s):  
Optimization Algorithm ◽  
Hyperspectral Images ◽  
Probabilistic Framework ◽  
Spatial Classification

Multi-scale superpixel spectral–spatial classification of hyperspectral images

2016 ◽  
Vol 37 (20) ◽  
pp. 4905-4922 ◽  
Author(s):  
Shanshan Li ◽  
Li Ni ◽  
Xiuping Jia ◽  
Lianru Gao ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Hyperspectral Images ◽  
Spatial Classification ◽  
Multi Scale

Evaluating the Potential of Clonal Selection Optimization Algorithm to Hyperspectral Image Feature Selection

2012 ◽  
Vol 500 ◽  
pp. 799-805 ◽  
Author(s):  
Farhad Samadzadegan ◽  
Shahin Rahmatollahi Namin ◽  
Mohammad Ali Rajabi
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Hyperspectral Image ◽  
Clonal Selection ◽  
Fitness Function ◽  
Hyperspectral Images ◽  
Image Feature ◽  
Spectral Bands ◽  
Selection Algorithms ◽  
Selection Of

The great number of captured near spectral bands in hyperspectral images causes the curse of dimensionality problem and results in low classification accuracy. The feature selection algorithms try to overcome this problem by limiting the input space dimensions of classification for hyperspectral images. In this paper, immune clonal selection optimization algorithm is used for feature selection. Also one of the fastest Artificial Immune classification algorithms is used to compute fitness function of the feature selection. The comparison of the feature selection results with genetic algorithm shows the clonal selection’s higher performance to solve selection of features.

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