Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Ahram Song"'
Autor:
Seula Park, Ahram Song
Publikováno v:
GIScience & Remote Sensing, Vol 60, Iss 1 (2023)
Existing methods of detecting building changes from very-high-resolution (VHR) images are limited by positional displacement. Although various change detection (CD) methods including deep learning methods have been proposed, they are incapable of ove
Externí odkaz:
https://doaj.org/article/8386829e06e04052b5310112bf4ba277
Autor:
Ahram Song
Publikováno v:
Aerospace, Vol 10, Iss 10, p 880 (2023)
Deep learning techniques have recently shown remarkable efficacy in the semantic segmentation of natural and remote sensing (RS) images. However, these techniques heavily rely on the size of the training data, and obtaining large RS imagery datasets
Externí odkaz:
https://doaj.org/article/c1a73ca436d34c46bbecf859e899e0e6
Autor:
Ahram Song, Yongil Kim
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 9, Iss 10, p 601 (2020)
Although semantic segmentation of remote-sensing (RS) images using deep-learning networks has demonstrated its effectiveness recently, compared with natural-image datasets, obtaining RS images under the same conditions to construct data labels is dif
Externí odkaz:
https://doaj.org/article/20eb07496b7541cc95d7fe08d01c7a75
Publikováno v:
Remote Sensing, Vol 12, Iss 15, p 2345 (2020)
Object-based image analysis (OBIA) is better than pixel-based image analysis for change detection (CD) in very high-resolution (VHR) remote sensing images. Although the effectiveness of deep learning approaches has recently been proved, few studies h
Externí odkaz:
https://doaj.org/article/b291bcb554d14c108fb499886da14969
Autor:
Ahram Song, Yongil Kim
Publikováno v:
Remote Sensing, Vol 12, Iss 7, p 1099 (2020)
Change detection (CD) networks based on supervised learning have been used in diverse CD tasks. However, such supervised CD networks require a large amount of data and only use information from current images. In addition, it is time consuming to man
Externí odkaz:
https://doaj.org/article/f02a62c86f314053a10acd812e320ae9
Autor:
Ahram Song, Jaewan Choi
Publikováno v:
Remote Sensing, Vol 12, Iss 5, p 799 (2020)
Remote sensing images having high spatial resolution are acquired, and large amounts of data are extracted from their region of interest. For processing these images, objects of various sizes, from very small neighborhoods to large regions composed o
Externí odkaz:
https://doaj.org/article/967ef72ed1f445c084ae858ee8523562
Autor:
Seula Park, Ahram Song
Publikováno v:
Remote Sensing, Vol 12, Iss 3, p 354 (2020)
The non-spatial information of cadastral maps must be repeatedly updated to monitor recent changes in land property and to detect illegal land registrations by tax evaders. Since non-spatial information, such as land category, is usually updated by f
Externí odkaz:
https://doaj.org/article/c1a145ee09804f10aba0e5a150225ae9
Publikováno v:
Sensors, Vol 15, Iss 2, Pp 2593-2613 (2015)
Pure surface materials denoted by endmembers play an important role in hyperspectral processing in various fields. Many endmember extraction algorithms (EEAs) have been proposed to find appropriate endmember sets. Most studies involving the automatic
Externí odkaz:
https://doaj.org/article/3cdb69f1739944219833d7fd95a63c2a
Publikováno v:
Remote Sensing, Vol 10, Iss 11, p 1827 (2018)
Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information
Externí odkaz:
https://doaj.org/article/f8df7375e04b425cb96a4d231cb56e93
Autor:
Ahram Song, Jaebin Lee
Publikováno v:
Sensors & Materials; 2024, Vol. 36 Issue 9, Part 3, p3997-4015, 19p