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Autore: | Das Monidipa |
Titolo: | Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements |
Pubblicazione: | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
Descrizione fisica: | 1 electronic resource (112 p.) |
Soggetto topico: | Research & information: general |
Environmental economics | |
Soggetto non controllato: | scene classification |
teacher-student | |
noisy labels | |
knowledge distillation | |
remote sensing images | |
LightGBM | |
spatiotemporal weight interpolation | |
AOD recovery | |
East Asia | |
polarized SAR | |
optical image | |
random forest | |
conditional random fields | |
feature-level fusion | |
Dirichlet process | |
infinite mixture models | |
Gamma distribution | |
variational inference | |
online setting | |
oil spill detection | |
synthetic aperture radar images | |
GNSS-R | |
CYGNSS | |
high wind speed inversion | |
SVR | |
PCA-SVR | |
CNN | |
Persona (resp. second.): | GhoshSoumya K |
ChowdaryV. M | |
MitraPabitra | |
RijalSantosh | |
DasMonidipa | |
Sommario/riassunto: | This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics. |
Titolo autorizzato: | Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910585940903321 |
Lo trovi qui: | Univ. Federico II |
Opac: | Controlla la disponibilità qui |