Classification of imbalanced hyperspectral images using ensembled kernel rotational forest
by Debaleena Datta; Pradeep Kumar Mallick; Mihir Narayan Mohanty
International Journal of Modelling, Identification and Control (IJMIC), Vol. 43, No. 2, 2023

Abstract: Hyperspectral image classification suffers from an imbalance in the samples belonging to its different classes. In this paper, we propose a two-fold novel approach named oversampler + kernel rotation forest (O + KRoF). First, Synthetic minority oversampling (SMOTE) and adaptive synthetic oversampling (ADASYN) techniques are employed on original data to balance it due to their adaptive nature in the majority and minority samples. Finally, the ensembled KRoF classifier is applied, a combination of unpruned classification and regression trees (CART) as its base algorithm and kernel PCA for feature reduction and most significant nonlinear spatial-spectral feature selection. Furthermore, we designed a comparison study with frequently used oversamplers and related state-of-art tree-based classifiers. However, it is found that our ensemble model is suitable and performs better as compared to earlier works as it attains 90.92%, 97.1%, and 93.39% overall accuracies when experimented on the benchmark datasets, Indian Pines, Salinas Valley, and Pavia University, respectively.

Online publication date: Sun, 30-Jul-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com