Open AccessImproved neural network classification of hyperspectral imagery using weighted genetic algorithm and hierarchical segmentation
Author(s) -
Akbari Davood
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5693
Subject(s) - hyperspectral imaging , pattern recognition (psychology) , computer science , artificial intelligence , artificial neural network , perceptron , support vector machine , benchmark (surveying) , subspace topology , genetic algorithm , segmentation , multilayer perceptron , algorithm , machine learning , geodesy , geography
This study proposes a modified spectral–spatial classification approach for improving the spectral–spatial classification of hyperspectral images. The spatial information is obtained by an enhanced marker‐based hierarchical segmentation (MHS) algorithm. The weighted genetic (WG) algorithm is first employed to obtain the subspace of hyperspectral data. The obtained features are then fed into the multi‐layer perceptron (MLP) neural network classification algorithm. Afterwards, the MHS algorithm is applied in order to increase the accuracy of less‐accurately classified land‐cover types. In the proposed approach, namely MLP‐MHS, the markers are extracted from the classification maps obtained by MLP and support vector machine classifiers. Experiments on two benchmark hyperspectral datasets, Pavia University and Berlin, validate the soundness of the proposed approach compared to the MLP and the original MHS algorithms.