Deep Learning Based Remote Sensing using Convolutional Neural Networks 
A. Shiny1, Mrinmoy Kumar Das2, Divyam Kumar Mishra3, Manish Kumar Singh4, Suman Maitra5
1A.Shiny, Department of CSE, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Mrinmoy Kumar Das, Department of CSE, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Divyam Kumar Mishra, Department of CSE, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
4Manish Kumar Singh, Department of CSE, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
5Suman Maitra, Department of CSE, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 25 August 2019 | Revised Manuscript received on 01 September 2019 | Manuscript Published on 14 September 2019 | PP: 102-106 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E10260785S319/19©BEIESP | DOI: 10.35940/ijeat.E1026.0785S319
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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: We describe our achievements in collecting alternating convergence points with a thickness of 7 μm and focal lengths of 200 and 350 mm, combined with shadow correction, deconvolution and significant neural frame training for transmission close to photography. Visual quality image. Although images taken using diffractive optics have been shown in previous papers, important neural structures have been used in the recovery phase. We use the imagery component of our imaging structure to activate the rise of ultralight cameras with remote identification for Nano and pico satellites, as well as small drones and solar-guided aircraft for aeronautical remote identification systems. . We extend the customizability of the liquid center focus on non-circular surfaces, forcing movement at the liquid convergence point of the surface. We study their trends and whether we can use them in optical structures.
Keywords: Deconvolution, Neural Framework, Picosatellites, Ultra-Lightweight Remote.
Scope of the Article: Deep Learning