Open AccessMorphology‐based structure‐preserving projection for spectral–spatial feature extraction and classification of hyperspectral data
Author(s) -
Imani Maryam,
Ghassemian Hassan
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.2017.1431
Subject(s) - hyperspectral imaging , pattern recognition (psychology) , artificial intelligence , feature extraction , computer science , spatial analysis , projection (relational algebra) , feature vector , mathematics , algorithm , statistics
Incorporation of spatial information besides rich spectral information of hyperspectral image significantly enhances data classification accuracy. A morphology‐based feature extraction and classification framework is proposed here, which includes the local neighbourhood information in a spatial window for extension of training set. The proposed method is morphology‐based structure‐preserving projection (MSPP) and tries to preserve the data structure in spectral–spatial feature space. Moreover, MSPP increases the class discrimination ability by defining a similarity matrix constructed by extended spectral–spatial training samples. The experimental results show the superiority of MSPP compared to some state‐of‐the‐art classification methods from the classification accuracy point of view.