Title: Thematic classification with support subspaces in hyperspectral images

Authors: Vladimir Alekseyevich Fursov; Sergey Alekseyevich Bibikov; Denis Alekseevich Zherdev; Nikolay Lvovich Kazanskiy

Addresses: IPSI RAS – Branch of the FSRC 'Crystallography and Photonics' RAS, 151, Molodogvardeyskaya, Samara, 443001, Russia; Samara National Research University, 34, Moskovskoye Shosse, Samara, 443086, Russia ' IPSI RAS – Branch of the FSRC 'Crystallography and Photonics' RAS, 151, Molodogvardeyskaya, Samara, 443001, Russia; Samara National Research University, 34, Moskovskoye Shosse, Samara, 443086, Russia ' IPSI RAS – Branch of the FSRC 'Crystallography and Photonics' RAS, 151, Molodogvardeyskaya, Samara, 443001, Russia; Samara National Research University, 34, Moskovskoye Shosse, Samara, 443086, Russia ' IPSI RAS – Branch of the FSRC 'Crystallography and Photonics' RAS, 151, Molodogvardeyskaya, Samara, 443001, Russia; Samara National Research University, 34, Moskovskoye Shosse, Samara, 443086, Russia

Abstract: In the study, a classification algorithm of plant crops in hyperspectral images is analysed. The algorithm uses the conjugation index with a subspace formed by samples of a given class. The purpose of the work is to show that this algorithm, with the data pre-processing (weighting of the feature vectors components and forming of the subclasses), provides a higher classification quality compared to the most popular reference vector method (SVM). The experiments were conducted with the implementation of the SVM method. The Indian Pines test of close types of vegetation, including 16 marked classes of plant crops, was used in the recognition experiments. The test was rather complicated, as class samples are highly correlated. The results show the possibility of a reliable recognition of plant crops.

Keywords: hyperspectral images; thematic classification; support vector machine; SVM; conjugation index.

DOI: 10.1504/IJESMS.2020.111268

International Journal of Engineering Systems Modelling and Simulation, 2020 Vol.11 No.4, pp.186 - 193

Received: 10 Oct 2019
Accepted: 16 Jan 2020
Published online: 17 Nov 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article