Remote Sensing Intelligent Interpretation for Geology

From Perspective of Geological Exploration

Xianju Li, Lizhe Wang, Weitao Chen, et al.

PDF
ca. 181,89

Springer Nature Singapore img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Geografie

Beschreibung

This book presents the theories and methods for geology intelligent interpretation based on deep learning and remote sensing technologies. The main research subjects of this book include lithology and mineral abundance.  

This book focuses on the following five aspects: 1. Construction of geology remote sensing datasets from multi-level (pixel-level, scene-level, semantic segmentation-level, prior knowledge-assisted, transfer learning dataset), which are the basis of geology interpretation based on deep learning. 2. Research on lithology scene classification based on deep learning, prior knowledge, and remote sensing. 3. Research on lithology semantic segmentation based on deep learning and remote sensing. 4. Research on lithology classification based on transfer learning and remote sensing. 5. Research on inversion of mineral abundance based on the sparse unmixing theory and hyperspectral remote sensing.  

The book is intended for undergraduate and graduate students who are interested in geology, remote sensing, and artificial intelligence. It is also used as a reference book for scientific and technological personnel of geological exploration.

Weitere Titel in dieser Kategorie
Cover Inequality and Mobility
Katharina Grüneisl
Cover Climate Justice
Cass R. Sunstein
Cover Contemporary Social Physics
Jitendra Kumar Pandey
Cover Liquid Democracy
Yu-Shan Tseng
Cover Liquid Democracy
Yu-Shan Tseng
Cover Turin's Olympic Legacy
Valerio della Sala
Cover Cassowary Dad
Beverley McWilliams
Cover The Atoms of Space
Balungi Francis

Kundenbewertungen

Schlagwörter

Interpretable deep learning, Lithology scene and segmentation classification, Fracture tectonic identification, Geology intelligent interpretation, Transfer learning, Geological exploration, Mineral abundance inversion, multimodal remote sensing