Change Detection using Deep Learning and Machine Learning Techniques for Multispectral Satellite Images
T. Vignesh1, K. K. Thyagharajan2, K. Ramya3
1T. Vignesh, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai (Tamil Nadu), India.
2K. K. Thyagharajan, RMD Engineering College, Kavaraipettai, Chennai (Tamil Nadu), India.
3K. Ramya, Technical Leader, Altran, Chennai (Tamil Nadu), India.
Manuscript received on 22 November 2019 | Revised Manuscript received on 03 December 2019 | Manuscript Published on 14 December 2019 | PP: 90-93 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10211191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1021.1191S19
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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: Change detection is used to find whether the changes happened or not between two different time periods using remote sensing images. We can use various machine learning techniques and deep learning techniques for the change detection analysis using remote sensing images. This paper mainly focused on computational and performance analysis of both techniques in the application of change detection .For each approach, we considered ten different kinds of algorithms and evaluated the performance. Moreover, in this research work, we have analyzed merits and demerits of each method which have used to change detection.
Keywords: Convolutional Neural Network (CNN), Image Averaging and Maximization, Discrete Cosine Transform, Remote Sensing.
Scope of the Article: Machine Learning