CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12552 > Article
Paper
10 February 2023 A clump tree instance segmentation based on PhenoCam imagery using MaskRCNN method
Mengying Cao, Qinchuan Xin
Author Affiliations +
Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 1255220 (2023) https://doi.org/10.1117/12.2667381
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China
Abstract
Vegetation is one of the important land covers and plays an important role in the ecosystem and climate change. Most of the current studies on vegetation and atmospheric change are based on the material and energy exchange between vegetation canopy and atmosphere. The study of the interaction between individual vegetation and atmosphere is affected by the limitations of acquisition. The development of the convolutional neural network derives a new approach for obtaining clump trees from PhenoCam. In this paper, a MaskRCNN method is proposed to train the model from PhenoCam images. Image enhancement processing is added to improve the training accuracy of the model. Then the training model is used to instance segment a clump tree from an individual PhoneCam imagery. The result of MaskRCNN instance segmentation mAP (mean average precision) can reach 0.887. In the vegetation boundary area and complex vegetation types, the effect of MaskRCNN segmentation is better than other methods. With the increase of image enhancement processing, the training accuracy of the model and the robustness of the data are enhanced. This study provides a feasible and efficient acquisition method for the clump tree segmentation.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Mengying Cao and Qinchuan Xin "A clump tree instance segmentation based on PhenoCam imagery using MaskRCNN method", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 1255220 (10 February 2023); https://doi.org/10.1117/12.2667381
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
;
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 6 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image segmentation
Vegetation
Image processing
Education and training
Data modeling
Image enhancement
Point clouds
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top