A Hybrid Algorithm for Urban LULC Change Detection for Building Smart-city by Using WorldView Images

Abstract

Technological advancement in smart cities can have adverse effects on the environment. Timely monitoring of smart cities to preserve environmental sustainability is a thrust area of research. It can be done by using change detection with multi-temporal satellite data. The success of these methods solely depends on the calibre of the backend image segmentation and Land-use Land-cover classification technique. The limitation of using cutting-edge classification algorithms is the availability of a proper dataset and identification of the edge structure of different LULC classes. In contrast, a segmentation algorithm cannot detect LULC classes automatically. In this research, we eliminated these shortcomings by considering a hybrid approach. We proposed a multi-class Support Vector Machine (SVM) and ISODATA-embedded large-scale change detection method. This method can automatically segment, detect, and perform LULC change analysis. We have considered the multi-sensor dataset of Barasat, West Bengal, India, obtained from the WorldView satellite sensor for the experimental study. The proposed method is validated concerning three different cutting-edge methods.

KEYWORDS:

• Change detection
• ISODATA
• Multi-class SVM
• Smart city
• VHR MS image

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Ramen Pal

Ramen Pal received his BE degree in information technology at the University of Burdwan, Burdwan, India in 2015. He completed his MTech degree in computer science and engineering at the University of Kalyani, India in 2018. He is currently pursuing PhD at the Department of Computer Science and Engineering, Assam University, Silchar, India. His areas of interest are soft computing, multi-spectral image processing, urban change analysis and portfolio optimization for stocks. Email: [email protected]

Somnath Mukhopadhyay

Somnath Mukhopadhyay is currently an assistant professor at the Department of Computer Science and Engineering, Assam University, Silchar, India. He completed his MTech and PhD degrees in computer science and engineering at the University of Kalyani, India, in 2011 and 2015, respectively. His areas of interest are image processing, remote sensing and soft computing.

Debasish Chakraborty

Debasish Chakraborty is the head Applications at Regional Remote Sensing Centre – East (RRSC-East), National Remote Sensing Centre (NRSC), Indian Space Research Organization (ISRO). He obtained PhD from Jadavpur University, Kolkata, India. His research interests include remote sensing, GIS, satellite image processing, machine learning and deep learning. He has been awarded with ISRO team excellence awards. Email: [email protected]

Ponnuthurai Nagaratnam Suganthan

Ponnuthurai Nagaratnam Suganthan (Fellow, IEEE) received the BA and MA degrees from the University of Cambridge, Cambridge, UK, and the PhD degree from Nanyang Technological University, Singapore. He was a recipient of the IEEE Transactions on Evolutionary Computation outstanding paper award in 2012, and the Highly cited Researcher Award by the Thomson Reuters in computer science in 2015. He is an associate editor of the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Information Sciences, and Pattern Recognition and the Founding co-editor-in-chief of Swarm and Evolutionary Computation. Email: [email protected]