Classification Methods for Remotely Sensed Data, 3rd Edition

• 0
• 1
• 2
• 3
• 4
• 5

The third edition of the bestselling Classification Methods for Remotely Sensed Data covers current state-of-the-art machine learning algorithms and developments in the analysis of remotely sensed data. This book is thoroughly updated to meet the needs of readers today and provides six new chapters on deep learning, feature extraction and selection, multisource image fusion, hyperparameter optimization, accuracy assessment with model explainability, and object-based image analysis, which is relatively a new paradigm in image processing and classification. It presents new AI-based analysis tools and metrics together with ongoing debates on accuracy assessment strategies and XAI methods.

New in this edition
Provides comprehensive background on the theory of deep learning and its application to remote sensing data.
Includes a chapter on hyperparameter optimization techniques to guarantee the highest performance in classification applications.
Outlines the latest strategies and accuracy measures in accuracy assessment and summarizes accuracy metrics and assessment strategies.

Discusses the methods used for explaining inherent structures and weighing the features of ML and AI algorithms that are critical for explaining the robustness of the models.

This book is intended for industry professionals, researchers, academics, and graduate students who want a thorough and up-to-date guide to the many and varied techniques of image classification applied in the fields of geography, geospatial and earth sciences, electronic and computer science, environmental engineering, etc.

---

Related News

Remote Sensing: Theory and Applications (True PDF)
Remote Sensing: Theory and Applications (True PDF) English | 2024 | ISBN: 1683927486 | 635 pages |... Image Processing and Machine Learning, Volume 1: Foundations of Image Processing
Image Processing and Machine Learning, Volume 1: Foundations of Image Processing English | 2024 |... Machine Learning Toolbox for Social Scientists Applied Predictive Analytics with R
Free Download Machine Learning Toolbox for Social Scientists by Aydede, Yigit; English | 2024 |... Data Labeling in Machine Learning with Python: Explore modern ways to prepare labeled data for training and fine-tuning ML
Data Labeling in Machine Learning with Python: Explore modern ways to prepare labeled data for...

System Comment

Facebook Comment