- We will send in 10–14 business days.
/images/books/14097535/9780443190773.jpg)
- Publisher: Elsevier
- ISBN-10: 0443190771
- ISBN-13: 9780443190773
- Format: 15.2 x 22.9 x 1.9 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Advances in Machine Learning and Image Analysis for Geoai (e-book) (used book) | bookbook.eu
Reviews
Description
Advances in Machine Learning and Image Analysis for GeoAI provides state-of-the-art machine learning and signal processing techniques for a comprehensive collection of geospatial sensors and sensing platforms. The book covers supervised, semi-supervised and unsupervised geospatial image analysis, sensor fusion across modalities, image super-resolution, transfer learning across sensors and time-points, and spectral unmixing among other topics. The chapters in these thematic areas cover a variety of algorithmic frameworks such as variants of convolutional neural networks, graph convolutional networks, multi-stream networks, Bayesian networks, generative adversarial networks, transformers and more.Advances in Machine Learning and Image Analysis for GeoAI provides graduate students, researchers and practitioners in the area of signal processing and geospatial image analysis with the latest techniques to implement deep learning strategies in their research.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 20d.18:03:03
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Publisher: Elsevier
- ISBN-10: 0443190771
- ISBN-13: 9780443190773
- Format: 15.2 x 22.9 x 1.9 cm, softcover
- Language: English English
Advances in Machine Learning and Image Analysis for GeoAI provides state-of-the-art machine learning and signal processing techniques for a comprehensive collection of geospatial sensors and sensing platforms. The book covers supervised, semi-supervised and unsupervised geospatial image analysis, sensor fusion across modalities, image super-resolution, transfer learning across sensors and time-points, and spectral unmixing among other topics. The chapters in these thematic areas cover a variety of algorithmic frameworks such as variants of convolutional neural networks, graph convolutional networks, multi-stream networks, Bayesian networks, generative adversarial networks, transformers and more.Advances in Machine Learning and Image Analysis for GeoAI provides graduate students, researchers and practitioners in the area of signal processing and geospatial image analysis with the latest techniques to implement deep learning strategies in their research.
Reviews