On Appraisal of Spectral Features Based Supervised Classifications for Hyperspectral Images
N. Aswini1, R. Ragupathy2
1N. Aswini*, Division of Computer and Information Science, Faculty of Science, Annamalai University, Annamalainagar, Tamil nadu, India.
2R. Ragupathy, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalainagar, Tamil nadu, India.
Manuscript received on February 02, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on March 30, 2020. | PP: 593-600 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7161038620/2020©BEIESP | DOI: 10.35940/ijrte.F7161.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The classification of hyperspectral images is a challenging task since it contains unbalanced ratio between the training and testing samples, and number of spectral bands. The detailed spectral data of hyperspectral images increases the ability to individualize the different classes and achieving accurate classification maps. Hence, in this paper, we use spectral data for classification and we address the performance of different supervised classification techniques like logic-based, ensemble-based, statistical-based, non-probabilistic-based and instance-based techniques on spectral features. Experiments are carried out using hyperspectral imagery captured by AVIRIS sensor such as Indian Pines, Salinas and Salinas-A. The appraisal of these supervised classification methods are held with each other in terms of performance metrics such as overall accuracy, precision, recall, F1-score and execution time.
Keywords: Hyperspectral Images, Logic Based Classifier, Ensemble Classifier, Non-Probabilistic Classifier, Statistical Classifier, Instance Based Classifier.
Scope of the Article: Logic, Functional programming and Microcontrollers for IoT.