Authors:
R. P. Pranav, R. P. Prawin, R. Subhashni, Sonjoy Ranjon Das 

Addresses:
1,2Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. 3Department of Computer Science and Applications, St. Peter’s Institute of Higher Education and Research,  Chennai, Tamil Nadu, India. 4Department of Computer Science, Shipley College, Shipley, England, United Kingdom. pranavramesh2004@gmail.com1, prawinrp2002@gmail.com2, subhashniraj2018@gmail.com3, sanjoy.das@shipley.ac.uk4

Abstract:

Advanced-Convolutional Neural Networks (A-CNNs) and modern computer vision technology are revolutionising remote sensing. This research revolutionises remote sensing visual identification by seamlessly merging cutting-edge sensor design and CNNs. We want to redefine remote sensing data use away from human-centric methods. Our substantial research into upgraded CNNs has given us unsurpassed image recognition accuracy and efficiency. Cutting-edge sensors that record stunning faraway images and provide contextual data for analysis have enhanced this achievement. Our extensive analysis includes sensor technology advances and the incorporation of CNN into remote sensing workflows. Thus, a comprehensive remote image analysis system for environmental monitoring, disaster management, and security enhancement has been created. The study showed great advances in distant picture processing and item detection. Our adaptable technique can recognise objects of interest, classify land cover, and track temporal change. Using sensor data efficiently with CNNs allows real-time, data-driven decision-making in many fields. In summary, our study ushers in a new era in distant sensing visual identification, entering undiscovered territory. With powerful CNNs and cutting-edge sensor design, remote image analysis and object detection can be improved across industries, ushering in a new era of innovation. 

Keywords: Visual Recognition; Convolutional Neural Networks (CNNs); Sensor Design; Image Analysis; Satellite Imagery; Object Detection; Computer Vision; Environmental Monitoring; Machine Interpretability; Innovative Sensor Systems.

Received on: 16/05/2023, Revised on: 27/08/2023, Accepted on: 13/11/2023, Published on: 22/12/2023

FMDB Transactions on Sustainable Computer Letters, 2023 Vol. 1 No. 4, Pages: 255-266

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