Details
| Original language | English |
|---|---|
| Title of host publication | 2022 10th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2022 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9781665470780 |
| Publication status | Published - 2022 |
| Externally published | Yes |
| Event | 10th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2022 - Quebec City, Canada Duration: 11 Jul 2022 → 14 Jul 2022 |
Abstract
Many geospatial applications rely on the extraction of spatial features, including road networks, from very high-resolution (VHR) satellite images. Researchers have developed many algorithms to achieve this goal, the majority of which are based on image fusion, fuzzy logic, and active contour models. The snake model is among the most widely used methods for road extraction by active contours. In most studies, an initial curve close to available roads is manually defined or based on prior knowledge. These methods also require manual adjustment of the snake model parameters, which is time-consuming. In order to address these limitations, this study proposes an algorithm for extracting roads from VHR satellite images in a semi-urban area that optimizes snake models by Honey-Bee Mating Optimization (HBMO). Based on a support vector machine and some image processing analysis, the presented method can extract an accurate initial curve, as well. According to the results of the experiments, the proposed approach not only eliminates the shortcomings of the snake model but also increases the accuracy of road extraction by 10% in all three study areas compared to the traditional snake method.
Keywords
- Edge Detection, HBMO Algorithm, Remote Sensing, Road Extraction, Snake Model
ASJC Scopus subject areas
- Environmental Science(all)
- Management, Monitoring, Policy and Law
- Agricultural and Biological Sciences(all)
- Agronomy and Crop Science
- Agricultural and Biological Sciences(all)
- Soil Science
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Computer Science(all)
- Information Systems
- Decision Sciences(all)
- Information Systems and Management
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2022 10th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2022. Institute of Electrical and Electronics Engineers Inc., 2022.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Optimizing the Snake Model Using Honey-Bee Mating Algorithm for Road Extraction from Very High-Resolution Satellite Images
AU - Sarmadian, Amin
AU - Moghimi, Armin
AU - Amani, Meisam
AU - Mahdavi, Sahel
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Many geospatial applications rely on the extraction of spatial features, including road networks, from very high-resolution (VHR) satellite images. Researchers have developed many algorithms to achieve this goal, the majority of which are based on image fusion, fuzzy logic, and active contour models. The snake model is among the most widely used methods for road extraction by active contours. In most studies, an initial curve close to available roads is manually defined or based on prior knowledge. These methods also require manual adjustment of the snake model parameters, which is time-consuming. In order to address these limitations, this study proposes an algorithm for extracting roads from VHR satellite images in a semi-urban area that optimizes snake models by Honey-Bee Mating Optimization (HBMO). Based on a support vector machine and some image processing analysis, the presented method can extract an accurate initial curve, as well. According to the results of the experiments, the proposed approach not only eliminates the shortcomings of the snake model but also increases the accuracy of road extraction by 10% in all three study areas compared to the traditional snake method.
AB - Many geospatial applications rely on the extraction of spatial features, including road networks, from very high-resolution (VHR) satellite images. Researchers have developed many algorithms to achieve this goal, the majority of which are based on image fusion, fuzzy logic, and active contour models. The snake model is among the most widely used methods for road extraction by active contours. In most studies, an initial curve close to available roads is manually defined or based on prior knowledge. These methods also require manual adjustment of the snake model parameters, which is time-consuming. In order to address these limitations, this study proposes an algorithm for extracting roads from VHR satellite images in a semi-urban area that optimizes snake models by Honey-Bee Mating Optimization (HBMO). Based on a support vector machine and some image processing analysis, the presented method can extract an accurate initial curve, as well. According to the results of the experiments, the proposed approach not only eliminates the shortcomings of the snake model but also increases the accuracy of road extraction by 10% in all three study areas compared to the traditional snake method.
KW - Edge Detection
KW - HBMO Algorithm
KW - Remote Sensing
KW - Road Extraction
KW - Snake Model
UR - http://www.scopus.com/inward/record.url?scp=85137890738&partnerID=8YFLogxK
U2 - 10.1109/agro-geoinformatics55649.2022.9859090
DO - 10.1109/agro-geoinformatics55649.2022.9859090
M3 - Conference contribution
AN - SCOPUS:85137890738
BT - 2022 10th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2022
Y2 - 11 July 2022 through 14 July 2022
ER -