Performance Evaluation Towards Automatic Building and Road Detection Technique for High-Resolution Remote Sensing Images

Abstract

Building and road detection from the high-resolution remote sensing images has many applications in a wide range of areas including urban design, real-estate management, and disaster relief. Extracting buildings and roads from the remote sensing images have been performed by human experts manually, but it takes too much time and the cost of labor to build is also very high. So, an automated system that can emulate a human operator is desired. Our goal is to develop a system for automatically detecting buildings and roads directly from high-resolution satellite images. Therefore, we propose Internal Gray Variance (IGV) to detect the buildings and roads in the urban areas. First, the satellite images are enhanced by using morphological operators, which enhance the edges of the objects. Then multiseed-based clustering technique detects the building and road edges using the variance in the gray levels. To reduce the false alarm, tiny regions that are improbable to be buildings and roads are separated. Finally, by using the adaptive threshold-based segmentation technique, the buildings are segmented and the road network is extracted based on supervised directional homogeneity. Finally, we evaluate our system on a large-scale road and building detection data sets that are publicly available.

KEYWORDS:

• Building detection
• Clustering
• High resolution
• Internal gray variance
• Remote sensing
• Segmentation
• Supervised directional homogeneity

Additional information

Notes on contributors

A. S. Radhamani

A S Radhamani received her under-graduate degree in electronics and communication engineering from Bharathiyar University and post-graduate degree in computer science and engineering from Manonmanium Sundaranar University in 1995 and 2004, respectively. She obtained her doctoral degree in computer science and engineering from Manonmanium Sundaranar University in 2015. She is the author of more than 25 publications. Her research interest includes multicore computing, parallel and distributed processing, cloud computing and image processing.

E. Baburaj

E Baburaj received his under-graduate and post-graduate in computer science and engineering from Madurai Kamaraj University. He obtained his Doctoral degree in computer science and engineering from Anna University Chennai. Currently, he is dean of PG studies and research of the Computer Science and Engineering Department, SCET, Nagercoil. His main research focuses on high performance and computer networks. More than 30 publications are credited to his name. Email: [email protected]