Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Christopher A. Ramezan"'
Autor:
Christopher A. Ramezan
Publikováno v:
Remote Sensing, Vol 14, Iss 24, p 6218 (2022)
Remote sensing analyses frequently use feature selection methods to remove non-beneficial feature variables from the input data, which often improve classification accuracy and reduce the computational complexity of the classification. Many remote se
Externí odkaz:
https://doaj.org/article/6a468c49d3ed48f9a2f1b8b69c72ace3
Publikováno v:
Remote Sensing, Vol 14, Iss 22, p 5760 (2022)
Many issues can reduce the reproducibility and replicability of deep learning (DL) research and application in remote sensing, including the complexity and customizability of architectures, variable model training and assessment processes and practic
Externí odkaz:
https://doaj.org/article/cc1a9532fd304f2295a00d7dee890304
Publikováno v:
Remote Sensing, Vol 13, Iss 3, p 368 (2021)
The size of the training data set is a major determinant of classification accuracy. Nevertheless, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be t
Externí odkaz:
https://doaj.org/article/1eef5d4c6fe240b8a88068c2766369f4
Autor:
Aaron E. Maxwell, Michelle S. Bester, Luis A. Guillen, Christopher A. Ramezan, Dennis J. Carpinello, Yiting Fan, Faith M. Hartley, Shannon M. Maynard, Jaimee L. Pyron
Publikováno v:
Remote Sensing, Vol 12, Iss 24, p 4145 (2020)
Historic topographic maps, which are georeferenced and made publicly available by the United States Geological Survey (USGS) and the National Map’s Historical Topographic Map Collection (HTMC), are a valuable source of historic land cover and land
Externí odkaz:
https://doaj.org/article/086d979b5eab4c428ba453c044a51855
Autor:
Aaron E. Maxwell, Michael P. Strager, Timothy A. Warner, Christopher A. Ramezan, Alice N. Morgan, Cameron E. Pauley
Publikováno v:
Remote Sensing, Vol 11, Iss 12, p 1409 (2019)
Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation data
Externí odkaz:
https://doaj.org/article/232e82a85de24621aba2ee93e06d4000
Publikováno v:
Remote Sensing, Vol 11, Iss 2, p 185 (2019)
High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to map land covers over large geographic areas using supervised machine learning algorithms. Although many studies have compared machine learning classification me
Externí odkaz:
https://doaj.org/article/9fec6fc65ee74a0c9ba4d4b06b3a5dce
Publikováno v:
Remote Sensing; Volume 13; Issue 3; Pages: 368
Remote Sensing, Vol 13, Iss 368, p 368 (2021)
Remote Sensing, Vol 13, Iss 368, p 368 (2021)
The size of the training data set is a major determinant of classification accuracy. Nevertheless, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5cf7350c48878cf1a572f38073904df
Autor:
Christopher A. Ramezan, Aaron E. Maxwell, Faith M. Hartley, Yiting Fan, Michelle S. Bester, Jaimee L. Pyron, Shannon Marie Maynard, Luis Andrés Guillén, Dennis J. Carpinello
Publikováno v:
Remote Sensing; Volume 12; Issue 24; Pages: 4145
Remote Sensing, Vol 12, Iss 4145, p 4145 (2020)
Remote Sensing, Vol 12, Iss 4145, p 4145 (2020)
Historic topographic maps, which are georeferenced and made publicly available by the United States Geological Survey (USGS) and the National Map’s Historical Topographic Map Collection (HTMC), are a valuable source of historic land cover and land
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b394d9fd53b2703bbcd9b922e35e4323
Publikováno v:
Photogrammetric Engineering & Remote Sensing. 83:737-747
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c4bf74e34524fa19d6d8346bfcccae0
https://doi.org/10.14358/pers.83.10.737
https://doi.org/10.14358/pers.83.10.737
