Open AccessRoad Identification Through Efficient Edge Segmentation Based on Morphological Operations
Author(s) -
Bandi Mary Sowbhagya Rani,
Vasumathi Devi Majety,
Chandra Shaker Pittala,
Vallabhuni Vijay,
K. Sandeep,
S Kiran
Publication year - 2021
Publication title -
traitement du signal/ts. traitement du signal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.279
H-Index - 11
eISSN - 1958-5608
pISSN - 0765-0019
DOI - 10.18280/ts.380526
Subject(s) - identification (biology) , computer science , segmentation , enhanced data rates for gsm evolution , computer vision , artificial intelligence , satellite , edge detection , image segmentation , remote sensing , satellite imagery , data mining , image processing , image (mathematics) , geography , engineering , botany , biology , aerospace engineering
Road identification from high-precision images is important to programmed mapping, urban planning, and updating geographic information system (GIS) databases. Manual identification of roads is slow, costly, and prone to errors. Therefore, it is a hot topic among remote sensing experts to develop programmed techniques for road identification from satellite images. The main challenge lies in the variation of width and surface contents between roads. This paper presents a road identification and extraction strategy for satellite images. The strategy, denoted as ESMIRMO, recognizes roads in satellite images through edge segmentation, using morphological operations. Specifically, morphological operations were employed to enhance the quality of the original image, laying a good basis for accurate road detection. Next, edge segmentation was adopted to detect the road in the original image accurately. After that, the proposed strategy was compared with traditional methods. The comparison shows that our strategy could identify roads from satellite images more accurately than traditional methods, and overcome many of their limitations. Overall, our strategy manages to enhance the quality of satellite images, pinpoint roads in the enhanced images, and provide drivers and repairers with real-time information on road conditions.