| Type of Material: | Thesis |
| Title: | Design and development of a model for classification and mapping of land use and land cover using multi spectral space born remote sensing images |
| Researcher: | Desai, Nirav H |
| Guide: | Shukla, Parag C |
| Department: | Computer Science |
| Publisher: | Atmiya University |
| Place: | Rajkot |
| Year: | 2023 |
| Language: | English |
| Subject: | Computer Science | Engineering and Technology | Remote Sensing | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Computer Science, Atmiya University, Rajkot; 2023 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-08 17:27:26 | |
| 008 | __ | 261008t2023||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464036 |
| 040 | __ | |dIN-AhILN|aATMI_360005 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aDesai, Nirav H |
| 110 | __ | |aComputer Science|0U-0967|dRajkot|bAtmiya University|ein |
| 245 | __ | |aDesign and development of a model for classification and mapping of land use and land cover using multi spectral space born remote sensing images |
| 260 | __ | |aRajkot|c2023|bAtmiya University |
| 300 | __ | |aA4|dDVD|c168 |
| 502 | __ | |bPhD|d2023|cComputer Science, Atmiya University, Rajkot |
| 518 | __ | |oDate of Award|d2023 |
| 518 | __ | |d2019|oDate of Registration |
| 518 | __ | |oDate of Completion|d2023 |
| 520 | __ | |aRemote sensing (RS) is the technique of finding and understanding information from a long distance or remote location using sensors. Land use and land cover mapping are fundamental tasks for planning and management. Identification of land cover and measurement of area under cultivation for various land use land cover is a very important task. However, techniques available for the above mention purpose are labor incentives, time-consuming, and costly. Remote sensing plays an important role in the mappings and classification of land cover features. Remote sensing provides very significant and sensed information. The deep neural network was used to perform the study. This study shows the semantic segmentation of LISS-III multispectral image using a fully convolutional network (FCN): U-net, Tiramisu, and Deeplabv3+. A multispectral image captures image data within specific wavelength ranges across the electromagnetic spectrum (EM). It has more than 100 nm resolution and less the 10 bands. Semantic Segmentation |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aRemote Sensing |
| 700 | __ | |aShukla, Parag C|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/522526|yShodhganga |
| 905 | __ | |afromsg |
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