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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15439
Title: Sub-scene Target Detection and Recognition Using Deep Learning Convolution Neural Networks
Authors: Merugu S.
Jain, Kamal
Mittal A.
Raman, Balasubramanian
Kumar A.
Paprzycki M.
Gunjan V.K.
Published in: Proceedings of Lecture Notes in Electrical Engineering
Abstract: Sub-scene recognition algorithm based on super resolution along with scene dependent neural network model and sub-scene dependent target detection for automating object information extraction is proposed. This work deals with large number of challenges possessed by classification problems. Some of the challenges in this problem are the low resolution satellite images, diverse pattern of each sub-scene causing the low level learning for classification and plethora of distinct object classes present in each sub-scene causes low accuracy of object detection. Objective of this paper presents an image super resolution technique for rectifying problems posed by low resolution images with color density variations of chromaticity coordinates. To eliminate the problem of diverse patterns, have divided various land cover types into separate groups based on maximum mixed fraction among these groups and corresponding sub-scene recognition disparate model parameters are used to recognize various scenes. To increase the accuracy for object detection has developed a sub-scene dependent Neural Network model for extracting the target/anomaly of object information. ¬© 2020, Springer Nature Singapore Pte Ltd.
Citation: Proceedings of Lecture Notes in Electrical Engineering, (2020), 1082- 1101
URI: https://doi.org/10.1007/978-981-15-1420-3_119
http://repository.iitr.ac.in/handle/123456789/15439
Issue Date: 2020
Publisher: Springer
Keywords: Chromaticity diagram
Classification
Neural network
Sub-pixel mapping
Sub-scene recognition
Super resolution
ISBN: 9.79E+12
ISSN: 18761100
Author Scopus IDs: 56658583800
56658294100
56763507700
23135470700
Author Affiliations: Merugu, S., R&D Centre, CMR College of Engineering & Technology, Hyderabad, India
Jain, K., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Mittal, A., Department of Computer Science and Engineeri
Corresponding Author: Merugu, S.; R&D Centre, CMR College of Engineering & TechnologyIndia; email: msuresh@cmrcet.org
Appears in Collections:Conference Publications [CE]
Conference Publications [CS]

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