An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

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Published: Jul 13, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i7s.7168
Keywords:
Image Enhancement, Edge Detection, Remote Sensing data, Jupyter tool, Classification

Main Article Content

Janarthanan. S
School of Computing Science and Engineering, Galgotias University, Greater Noida, India
T. Ganesh Kumar
School of Computing Science and Engineering, Galgotias University, Greater Noida, India
K. Uma
School of Information Technology, VIT University,Vellore, India
M. Arvindhan
School of Computing Science and Engineering, Galgotias University, Greater Noida, India
Anandhan. K
Department of Computer Science and Engineering, GITAM University, Bengaluru
C. Ramesh Kumar
School of Computing Science and Engineering, Galgotias University, Greater Noida, India

Abstract

The main process of this image classification with a convolution neural network using deep learning model was performed in the programming language Python code in the Jupyter tool, mainly using the data set of IRS P-6 LISS IV from an Indian remote sensing satellite with a high resolution multi-spectral camera with around 5.8m from an 817 km altitude Delhi image. To classify the areas within the cropped image required to apply enhancement techniques, the image size was 1000 mb. To view this image file required high-end software for opening. For that, initially, ERDAS imaging software viewer was used for cropping into correct resolution pixels. based on that cropped image used for image classification with preprocessing for applying filters for enhancement. And with the convolution neural network model, required to train the sample images of the same pixels, was collected from the group of objects that were cropped. Then we needed to use image sample areas to train the model with learning rate and epoch rate to improve object detection accuracy using the Jupyter notebook tool with tensorflow and machine learning model produce the accuracy rate of 90.78%.

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How to Cite
S, J., Kumar, T. G. ., Uma, K. ., Arvindhan, M., K, A., & Kumar, C. R. . (2023). An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 604–609. https://doi.org/10.17762/ijritcc.v11i7s.7168
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Vol. 11 No. 7s (2023)
Section
Articles

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