Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Geographic Object-Based Image Analysis: A Primer and Future Directions
by
Ghaffarian, Salar
, Kucharczyk, Maja
, Hugenholtz, Chris H.
, Hay, Geoffrey J.
in
Accuracy
/ Artificial neural networks
/ Classification
/ Deep learning
/ GEOBIA
/ Geographic information systems
/ geographic object-based image analysis
/ Image analysis
/ Image processing
/ machine learning
/ Neural networks
/ OBIA
/ Object recognition
/ object-based image analysis
/ Pixels
/ Remote sensing
/ Reviews
/ Source code
/ Workflow
2020
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Geographic Object-Based Image Analysis: A Primer and Future Directions
by
Ghaffarian, Salar
, Kucharczyk, Maja
, Hugenholtz, Chris H.
, Hay, Geoffrey J.
in
Accuracy
/ Artificial neural networks
/ Classification
/ Deep learning
/ GEOBIA
/ Geographic information systems
/ geographic object-based image analysis
/ Image analysis
/ Image processing
/ machine learning
/ Neural networks
/ OBIA
/ Object recognition
/ object-based image analysis
/ Pixels
/ Remote sensing
/ Reviews
/ Source code
/ Workflow
2020
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Geographic Object-Based Image Analysis: A Primer and Future Directions
by
Ghaffarian, Salar
, Kucharczyk, Maja
, Hugenholtz, Chris H.
, Hay, Geoffrey J.
in
Accuracy
/ Artificial neural networks
/ Classification
/ Deep learning
/ GEOBIA
/ Geographic information systems
/ geographic object-based image analysis
/ Image analysis
/ Image processing
/ machine learning
/ Neural networks
/ OBIA
/ Object recognition
/ object-based image analysis
/ Pixels
/ Remote sensing
/ Reviews
/ Source code
/ Workflow
2020
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Geographic Object-Based Image Analysis: A Primer and Future Directions
Journal Article
Geographic Object-Based Image Analysis: A Primer and Future Directions
2020
Request Book From Autostore
and Choose the Collection Method
Overview
Geographic object-based image analysis (GEOBIA) is a remote sensing image analysis paradigm that defines and examines image-objects: groups of neighboring pixels that represent real-world geographic objects. Recent reviews have examined methodological considerations and highlighted how GEOBIA improves upon the 30+ year pixel-based approach, particularly for H-resolution imagery. However, the literature also exposes an opportunity to improve guidance on the application of GEOBIA for novice practitioners. In this paper, we describe the theoretical foundations of GEOBIA and provide a comprehensive overview of the methodological workflow, including: (i) software-specific approaches (open-source and commercial); (ii) best practices informed by research; and (iii) the current status of methodological research. Building on this foundation, we then review recent research on the convergence of GEOBIA with deep convolutional neural networks, which we suggest is a new form of GEOBIA. Specifically, we discuss general integrative approaches and offer recommendations for future research. Overall, this paper describes the past, present, and anticipated future of GEOBIA in a novice-accessible format, while providing innovation and depth to experienced practitioners.
Publisher
MDPI AG
Subject
This website uses cookies to ensure you get the best experience on our website.