Iceberg Detection in Satellite Images using Deep Learning Techniques
Aravapalli Naveena1, J.V.D.Prasad2
1Aravapalli Naveena, M.Tech, Department of Computer Science and Engineering, V R Siddhartha Engineering College, Vijayawada.
2J.V.D.Prasad , Pursuing Ph.D, Acharya Nagarjuna University.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4701-4704 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9736038620 /2020©BEIESP | DOI: 10.35940/ijrte.F9736.038620
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Iceberg detection is found to be more critical in the previous researchers. High quality satellite monitoring of dangerous ice formations is critical to navigation safety and economic activity in the regions. The satellite images play a crucial role in the identification of the icebergs. In this manuscript, a convolutional neural network (CNN) model is proposed for the iceberg detection from the satellite images. It is based on the satellite dataset for target classification and target identification. The iceberg detection is based on the statistical criteria for finding the satellite images. This model is used to identify automatically whether it is remote sensed target is iceberg or not. Sometimes the iceberg is wrongly classified as ship. This model is done to make accurate about the changes in the detection.
Keywords: Convolutional Neural Network (CNN), Iceberg Detection, Satellite Images, Target Classification.
Scope of the Article: Classification.