Open AccessGraph‐based spatial–spectral feature learning for hyperspectral image classification
Author(s) -
Ahmad Muhammad,
Khan Adil Mehmood,
Hussain Rasheed
Publication year - 2017
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.2017.0168
Subject(s) - hyperspectral imaging , pattern recognition (psychology) , artificial intelligence , multiple kernel learning , kernel (algebra) , computer science , graph , consistency (knowledge bases) , curse of dimensionality , feature vector , graph kernel , mathematics , kernel method , support vector machine , kernel principal component analysis , theoretical computer science , combinatorics
Classifying hyperspectral data within high dimensionality is a challenging task. To cope with this issue, this study implements a semi‐supervised multi‐kernel class consistency regulariser graph‐based spatial–spectral feature learning framework. For feature learning process, establishing the neighbouring relationship between the distinct samples from the high‐dimensional space is the key to a favourable outcome for classification. The proposed method implements two kernels and a class consistency regulariser. The first kernel constructs simple edges where every single vertex represents one particular sample and the edge weight encodes the initial similarity between distinct samples. Later the obtained relation is fed into the second kernel to obtain the final features for classification where the semi‐supervised learning is conducted to estimate the grouping relations among different samples according to their similarity, class, and spatial information. To validate the performance of proposed framework, the authors conduct several experiments on three publically available hyperspectral datasets. The proposed work equates favourably with state‐of‐the‐art works with an overall classification accuracy of 98.54, 97.83, and 98.38% for Pavia University, Salinas‐A, and Indian Pines datasets, respectively.