Open AccessDeep Learning Based Remote Sensing using Convolutional Neural Networks
Author(s) -
A.Sahaya Shiny,
Mrinmoy Kumar Das,
Divyam Kumar Mishra,
Manish Kumar Singh,
S. C. Maitra
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e1026.0785s319
Subject(s) - convolutional neural network , computer science , focus (optics) , deconvolution , convergence (economics) , identification (biology) , remote sensing , shadow (psychology) , artificial neural network , artificial intelligence , frame (networking) , computer vision , deep learning , image quality , image (mathematics) , optics , geology , physics , telecommunications , algorithm , psychology , botany , economics , biology , economic growth , psychotherapist
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.