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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/21733
Title: A Deep Learning Framework Approach for Urban Area Classification Using Remote Sensing Data
Authors: Nijhawan R.
Jindal R.
Sharma H.
Raman, Balasubramanian
Das, Josodhir D.
Chaudhuri B.B.
Chaudhuri B.B.
Nakagawa M.
Khanna P.
Kumar S.
Published in: Advances in Intelligent Systems and Computing
3rd International Conference on Computer Vision and Image Processing, CVIP 2018
Abstract: The main aim of this study is to propose a Deep Learning framework approach for Urban area classification. The research proposes a multilevel Deep Learning architecture to detect the Urban/Non-Urban Area. The support models/parameters of the structure are Support Vector Machine (SVM), convolution of (Neural Networks) NN, high resolution sentinel 2 data, and several texture parameters. The experiments were conducted for the study region Lucknow which is a fast-growing metropolis of India, using Sentinel 2 satellite data of spatial resolution 10-m. The performance observed by the proposed ensembles of CNNs outperformed those of current state of art machine algorithms viz; SVM, Random Forest (RF) and Artificial Neural Network (ANN). It was observed that our Proposed Approach (PA) furnished the maximum classification accuracy of 96.24%, contrasted to SVM (65%), ANN (84%) and RF (88%). Several statistical parameters namely accuracy, specificity, sensitivity, precision and AUC, have been evaluated for examining performance during training and validation phase of the models. © 2020, Springer Nature Singapore Pte Ltd.
Citation: Advances in Intelligent Systems and Computing (2020), 1022 AISC: 449-456
URI: https://doi.org/10.1007/978-981-32-9088-4_37
http://repository.iitr.ac.in/handle/123456789/21733
Issue Date: 2020
Publisher: Springer Science and Business Media Deutschland GmbH
Keywords: Convolution neural network
Deep learning
Remote sensing
Support vector machine
Urban area classification
ISBN: 9.79E+12
ISSN: 21945357
Author Scopus IDs: 57192176367
57224312903
57202620314
23135470700
7202105464
Author Affiliations: Nijhawan, R., Department of Computer Science and Engineering, Graphic Era University, Dehradun, India
Jindal, R., Jaypee Institute of Information Technology, Noida, India
Sharma, H., National Institute of Technology Hamirpur, Hamirpur, India
Raman, B., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Das, J., Department of Computer Science and Engineering, Graphic Era University, Dehradun, India, Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Corresponding Author: Sharma, H.; National Institute of Technology HamirpurIndia; email: sharmah70@gmail.com
Appears in Collections:Conference Publications [CS]

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