Abstract
With the ever-expanding urbanization taking place everywhere, it has become the need of the hour to have an automated classification mechanism to track the development with the least time and effort. Nowadays, machine learning and deep learning tools have eased out the efforts for image classification and processing in analytics. When discussing classification to monitor the urban growth or the amount of construction being done, to be specific, the last thing is working on multi-resolution satellite data. It aids in providing spatial information about the targeted place or region of interest (ROI). Hence, deep learning has become the most progressive model for remote sensing applications. The paper presents the research carried out to classify a multi-resolution satellite image into constructed or built-up and non-constructed or non- built-up areas using deep neural networks. The Convolutional neural network (CNN) algorithm is one such methodology that has shown to give favorable results on satellite imagery as well. In this research, CNN was used to binary the ROI into built-up and non-built-up classes. The algorithm was trained and tested on 2 multi-resolution satellite datasets of Bangalore and Bombay cities. The sequential CNN model was used for feature extraction and classification. The average accuracy obtained was around 0.95 (95%), P-Score, R-Score, and the kappa value stood at an average of 0.851, 0.785, and 0.792, respectively, when tested on different networks parameters.
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Acknowledgments
The work presented in this paper is carried out as a part of the funded research project of the Indian Space Research Organization (ISRO), RESPOND (OGP198). The authors would like to express their sincere gratitude to the Principal and members of the Centre of Excellence in multimedia signal processing (COEMSP), A. D. Patel Institute of Technology, New V. V. Nagar, for providing all the necessary support.
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Shukla, J., Patel, H., Jain, G., Sharma, S. (2022). Built-Up Area Extraction on Multispectral Satellite Data Using Simple CNN. In: Iyer, B., Crick, T., Peng, SL. (eds) Applied Computational Technologies. ICCET 2022. Smart Innovation, Systems and Technologies, vol 303. Springer, Singapore. https://doi.org/10.1007/978-981-19-2719-5_8
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