Combining "Deep Learning" and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges. Herzfeld, U. C., Hessburg, L. J., Trantow, T. M., & Hayes, A. N. Remote. Sens., 16(11):1854, 06, 2024. Link Paper bibtex @article{journals/remotesensing/HerzfeldHTH24,
added-at = {2024-07-19T00:00:00.000+0200},
author = {Herzfeld, Ute C. and Hessburg, Lawrence J. and Trantow, Thomas M. and Hayes, Adam N.},
biburl = {https://www.bibsonomy.org/bibtex/2eec03bed805254367e9761133b7d756b/dblp},
ee = {https://doi.org/10.3390/rs16111854},
interhash = {bb5cffa01b7ceed2a6d2dc402887fa6c},
intrahash = {eec03bed805254367e9761133b7d756b},
journal = {Remote. Sens.},
keywords = {dblp},
month = {06},
number = 11,
pages = 1854,
timestamp = {2024-07-22T07:03:25.000+0200},
title = {Combining "Deep Learning" and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges.},
url = {http://dblp.uni-trier.de/db/journals/remotesensing/remotesensing16.html#HerzfeldHTH24},
volume = 16,
year = 2024
}
Downloads: 0
{"_id":"W3AhxxfbDNQPCwCmu","bibbaseid":"herzfeld-hessburg-trantow-hayes-combiningdeeplearningandphysicallyconstrainedneuralnetworkstoderivecomplexglaciologicalchangeprocessesfrommodernhighresolutionsatelliteimageryapplicationofthegeoclassimagesystemtocreatevariocnnforglaciersurges-2024","author_short":["Herzfeld, U. C.","Hessburg, L. J.","Trantow, T. M.","Hayes, A. N."],"bibdata":{"bibtype":"article","type":"article","added-at":"2024-07-19T00:00:00.000+0200","author":[{"propositions":[],"lastnames":["Herzfeld"],"firstnames":["Ute","C."],"suffixes":[]},{"propositions":[],"lastnames":["Hessburg"],"firstnames":["Lawrence","J."],"suffixes":[]},{"propositions":[],"lastnames":["Trantow"],"firstnames":["Thomas","M."],"suffixes":[]},{"propositions":[],"lastnames":["Hayes"],"firstnames":["Adam","N."],"suffixes":[]}],"biburl":"https://www.bibsonomy.org/bibtex/2eec03bed805254367e9761133b7d756b/dblp","ee":"https://doi.org/10.3390/rs16111854","interhash":"bb5cffa01b7ceed2a6d2dc402887fa6c","intrahash":"eec03bed805254367e9761133b7d756b","journal":"Remote. Sens.","keywords":"dblp","month":"06","number":"11","pages":"1854","timestamp":"2024-07-22T07:03:25.000+0200","title":"Combining \"Deep Learning\" and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges.","url":"http://dblp.uni-trier.de/db/journals/remotesensing/remotesensing16.html#HerzfeldHTH24","volume":"16","year":"2024","bibtex":"@article{journals/remotesensing/HerzfeldHTH24,\n added-at = {2024-07-19T00:00:00.000+0200},\n author = {Herzfeld, Ute C. and Hessburg, Lawrence J. and Trantow, Thomas M. and Hayes, Adam N.},\n biburl = {https://www.bibsonomy.org/bibtex/2eec03bed805254367e9761133b7d756b/dblp},\n ee = {https://doi.org/10.3390/rs16111854},\n interhash = {bb5cffa01b7ceed2a6d2dc402887fa6c},\n intrahash = {eec03bed805254367e9761133b7d756b},\n journal = {Remote. Sens.},\n keywords = {dblp},\n month = {06},\n number = 11,\n pages = 1854,\n timestamp = {2024-07-22T07:03:25.000+0200},\n title = {Combining \"Deep Learning\" and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges.},\n url = {http://dblp.uni-trier.de/db/journals/remotesensing/remotesensing16.html#HerzfeldHTH24},\n volume = 16,\n year = 2024\n}\n\n","author_short":["Herzfeld, U. C.","Hessburg, L. J.","Trantow, T. M.","Hayes, A. N."],"key":"journals/remotesensing/HerzfeldHTH24","id":"journals/remotesensing/HerzfeldHTH24","bibbaseid":"herzfeld-hessburg-trantow-hayes-combiningdeeplearningandphysicallyconstrainedneuralnetworkstoderivecomplexglaciologicalchangeprocessesfrommodernhighresolutionsatelliteimageryapplicationofthegeoclassimagesystemtocreatevariocnnforglaciersurges-2024","role":"author","urls":{"Link":"https://doi.org/10.3390/rs16111854","Paper":"http://dblp.uni-trier.de/db/journals/remotesensing/remotesensing16.html#HerzfeldHTH24"},"keyword":["dblp"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"article","biburl":"http://www.bibsonomy.org/bib/author/Thomas?items=1000","dataSources":["Wze2LBds9bM8uwxm8"],"keywords":["dblp"],"search_terms":["combining","deep","learning","physically","constrained","neural","networks","derive","complex","glaciological","change","processes","modern","high","resolution","satellite","imagery","application","geoclass","image","system","create","variocnn","glacier","surges","herzfeld","hessburg","trantow","hayes"],"title":"Combining \"Deep Learning\" and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges.","year":2024}