Improved Convolutional Neural Networks for the Classification of the Hyperspectral Image
Main Article Content
Abstract
In recent times, convolutional neural network (CNN) provides improved performance on various image processing analysis. This includes classification of images even with redundant information over various imaging application. With such aim, in this paper, the hyperspectral images are classified using CNN in spectral domain. The CNN architecture includes five different layers enables the classification of data samples by the CNN classifiers and discards redundant information. The experimental results test the efficacy of the model, where the results show that the CNN obtains higher classification accuracy than other methods.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.