| Type of Material: | Thesis |
| Title: | Develop an automatic road network extraction system from remote sensing images |
| Researcher: | Patel, Miral J |
| Guide: | Kothari, Ashish M |
| Department: | Electronics and Communication Engineering |
| Publisher: | Atmiya University |
| Place: | Rajkot |
| Year: | 2023 |
| Language: | English |
| Subject: | Engineering and Technology | Engineering Electrical and Electronic | Image Processing | Semantic Segmentation | Deep Learning | Engineering | Electronics and Communication Engineering | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Electronics and Communication Engineering, Atmiya University, Rajkot; 2023 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-08 17:24:30 | |
| 008 | __ | 261008t2023||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464029 |
| 040 | __ | |dIN-AhILN|aATMI_360005 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aPatel, Miral J |
| 110 | __ | |aElectronics and Communication Engineering|0U-0967|dRajkot|bAtmiya University|ein |
| 245 | __ | |aDevelop an automatic road network extraction system from remote sensing images |
| 260 | __ | |aRajkot|c2023|bAtmiya University |
| 300 | __ | |a100|dCD|cA4 |
| 502 | __ | |bPhD|d2023|cElectronics and Communication Engineering, Atmiya University, Rajkot |
| 518 | __ | |oDate of Award|d2023 |
| 518 | __ | |d2019|oDate of Registration |
| 518 | __ | |d2023|oDate of Completion |
| 520 | __ | |aIn recent years, both the development of high resolution satellite images and the amount of available aerial images have expanded and accessible easily. Unfortunately, the technologies used to analyze all of these images have not kept pace, so a large portion of the job is still done manually by humans, which is costly, time-consuming, and error-prone. Due to these factors, there is a strong need for efficient and dependable techniques that can automatically analyze remote sensing images. However, even in high-resolution remote sensing images, background and roadways might be difficult to distinguish from complex background images because of the occlusion of trees and buildings. Now a day, Deep Learning, which has the computing power for massive data, has emerged as the most popular and effective classification approach. Two different novel methods are developed for automatic road surface detection from high- resolution remote sensing images based on deep learning that detect effectively, efficiently and fa |
| 650 | __ | |aElectronics and Communication Engineering|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aEngineering Electrical and Electronic |
| 653 | __ | |aImage Processing |
| 653 | __ | |aSemantic Segmentation |
| 653 | __ | |aDeep Learning |
| 653 | __ | |aEngineering |
| 700 | __ | |aKothari, Ashish M|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/472566|yShodhganga |
| 905 | __ | |afromsg |
User Feedback Comes Under This section.