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Deep learning in Remote sensing
Details
In this book, an overview of DL is presented that adopts various perspectives such as state-of-the-arts deep learning techniques, Deep learning approaches, applications. Additionally, the potential problems on deep learning technology. This research presents convolutional neural networks (CNNs) which the most utilized DL network type. A survey of the CNN deep learning architectures that are frequently encountered in the literature, along with their strengths and limitations and describes the development of CNNs architectures together with their main features, e.g., AlexNet, VGG, ResNet, DenseNet, GoogLeNet, Inception: ResNet nd Inception V3/ V4, SegNet, U Net, Point CNN and MASK R-CNN .A detailed study on application of Convolutional Neural Network on the remote sensing to extract features is also explained. Challenges that met CNN were discussed.
Autorentext
prof.Lamyaa Gamal Eldeen TahaHead of the Aviation and aerial photography division National Authority for Remote Sensing and Space SciencesDr. Rania El-sayed Ibrahim Head of Documentation and scientific publishingNational Authority for Remote Sensing and Space SciencesEng.Asmaa Ahmed MandouhNational Authority for Remote Sensing and Space Sciences
Weitere Informationen
- Allgemeine Informationen
- GTIN 09786207447008
- Genre Earth Science
- Anzahl Seiten 60
- Herausgeber LAP LAMBERT Academic Publishing
- Größe H220mm x B150mm
- Jahr 2023
- EAN 9786207447008
- Format Kartonierter Einband
- ISBN 978-620-7-44700-8
- Titel Deep learning in Remote sensing
- Autor Lamyaa Taha , Rania Ibrahim , Asmaa Mandouh
- Untertitel Convolutional Neural Network (CNNs)
- Sprache Englisch