A hybrid algorithm for urban LULC change detection for building smart-city by using Worldview images

• Main Authors: Pal, Ramen, Mukhopadhyay, Somnath, Chakraborty, Debasish, Suganthan, Ponnuthurai Nagaratnam
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Electrical and electronic engineering; Smart City; Change Detection
• Online Access: https://hdl.handle.net/10356/172519
• Institution: Nanyang Technological University

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.