International Journal of Application or Innovation in Engineering & Management
An Inspiration for Recent Innovation & Research….
ISSN 2319 – 4847
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Call for Paper, Published Articles, Indexing Infromation Remote Sensing Image Category Classification Using Deep Learning , Authors : Dr L.JabaSheela, R.Banupriya, G.Dilisha, J.Princy, International Journal of Application or Innovation in Engineering & Management (IJAIEM), www.ijaiem.org
Volume & Issue no: Volume 9, Issue 3, March 2020

Title:
Remote Sensing Image Category Classification Using Deep Learning
Author Name:
Dr L.JabaSheela, R.Banupriya, G.Dilisha, J.Princy
Abstract:
ABSTRACT The remote sensing Image Classification plays a major role in real-time applications. Deep Learning plays a vital role in different fields such as Natural language Processing, Computer Vision medical fields, and Image classification. Compared to the machine learning algorithms, deep networks provide higher accuracy, strong ability to learn data extraction. Geographical satellite images that are utilized for the investigation of environmental and geological fields are acquired through remote sensing techniques. The rough pictures accumulated from the satellites are not suitable for authentic assessment and exact report plans, so crude pictures experience the customary picture grouping systems, for example, information preprocessing, division, information include extraction and characterization. The old picture characterization techniques have spatial and otherworldly goals issues. The most recent picture order strategy specifically profound CNN systems. The CNN algorithm classifies the images into various categories namely water, land, forest, agricultural area. Keywords: Image Classification, Enhancement, Remote Sensing, Resolution, Satellite Sensors, deep learning, Convolution neural networks.
Cite this article:
Dr L.JabaSheela, R.Banupriya, G.Dilisha, J.Princy , " Remote Sensing Image Category Classification Using Deep Learning " , International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 9, Issue 3, March 2020 , pp. 066-073 , ISSN 2319 - 4847.
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