Object & Pattern Recognition In Remote Sensing (Hardcover)

Prof. Stefan Hinz

ISBN 10: 1849951284 / ISBN 13: 9781849951289

Published by Porto Press Ltd, Caithness, 2020

About this Item

Hardcover. Fully automated interpretation and understanding of remotely sensed data by a computer has been a challenge for many decades, and many approaches have been developed over the years. Significant advances in knowledge-based image understanding, machine learning and artificial intelligence has led to this topic being the focus of much research in recent years. This book highlights the different theoretical and application-oriented aspects and potential solutions to the topic of automated remote sensing data analysis. Thereby, both classical knowledge-based as well as modern machine learning-oriented concepts are described. A field such as this is specialized and dynamic and also interdisciplinary and multilayered. Written by an international team of experts, the book has therefore been split into parts dealing with the concepts and applications, and the focus is on elucidating the complementarity of different lines of research rather than providing the complete set of scientific approaches.Part A of this book gives insight into the basic theories and concepts of feature extraction, image understanding and the respective assessment strategies as well as into geometric, radiometric and sensor-related fundamentals of remote sensing technology. Part B focuses on various scientific and practical applications of remote sensing data analysis. These range from the automatic detailed reconstruction of complex 3D environments to visual tracking of objects in image sequences as well as monitoring natural and anthropogenic long-term processes on a regional scale. Part C sketches recent trends in automatic analysis of remote sensing data. This book highlights the different theoretical and application-oriented aspects and potential solutions to the topic of automated remote sensing data analysis. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781849951289

Bibliographic Details

• Title: Object & Pattern Recognition In Remote Sensing (Hardcover)
• Publisher: Porto Press Ltd, Caithness
• Publication Date: 2020
• Language: English
• ISBN 10: 1849951284
• ISBN 13: 9781849951289
• Binding: Hardcover
• Condition: new

About this title

Synopsis

Fully automated interpretation and understanding of remotely sensed data by a computer has been a challenge for many decades, and many approaches have been developed over the years. Significant advances in knowledge-based image understanding, machine learning and artificial intelligence has led to this topic being the focus of much research in recent years.

This book highlights the different theoretical and application-oriented aspects and potential solutions to the topic of automated remote sensing data analysis. Thereby, both classical knowledge-based as well as modern machine learning-oriented concepts are described. A field such as this is specialized and dynamic and also interdisciplinary and multilayered. Written by an international team of experts, the book has therefore been split into parts dealing with the concepts and applications, and the focus is on elucidating the complementarity of different lines of research rather than providing the complete set of scientific approaches.

Part A of this book gives insight into the basic theories and concepts of feature extraction, image understanding and the respective assessment strategies as well as into geometric, radiometric and sensor-related fundamentals of remote sensing technology. Part B focuses on various scientific and practical applications of remote sensing data analysis. These range from the automatic detailed reconstruction of complex 3D environments to visual tracking of objects in image sequences as well as monitoring natural and anthropogenic long-term processes on a regional scale. Part C sketches recent trends in automatic analysis of remote sensing data.