MFFENet and ADANet: a robust deep transfer learning method and its application in high precision and...
MFFENet and ADANet: a robust deep transfer learning method and its application in high precision and fast cross-scene recognition of earthquake-induced landslides
About this item
Full title
Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Automatic recognition and segmentation methods have become an essential requirement in identifying large-scale earthquake-induced landslides. This used to be conducted through pixel-based or object-oriented methods. However, these methods fail to develop an accurate, rapid, and cross-scene solution for earthquake-induced landslide recognition becau...
Alternative Titles
Full title
MFFENet and ADANet: a robust deep transfer learning method and its application in high precision and fast cross-scene recognition of earthquake-induced landslides
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2674131809
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2674131809
Other Identifiers
ISSN
1612-510X
E-ISSN
1612-5118
DOI
10.1007/s10346-022-01847-1
