CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 11428 > Article
Paper
14 February 2020 A novel self-relearning approach for Landsat image change detection
Xiaoyu Chang, Xin Huang, Jiayi Li
Author Affiliations +
Proceedings Volume 11428, MIPPR 2019: Multispectral Image Acquisition, Processing, and Analysis; 114280J (2020) https://doi.org/10.1117/12.2538192
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China
Abstract
Land cover composition and change are important aspects for many scientific research and socioeconomic assessments. The multi-date land cover change detection is generally more difficult and time-consuming to select enough training samples when considering multi-date image labels at the same location. To improve the accuracy for multi-date change detection, this study proposed a new algorithm framework, combining self-learning and relearning algorithm. Wuhan was selected as the experimental area, and Landsat images in 2005 and 2016 were used to extract six main types of change classes. Firstly, PCM (primitive co-occurrence matrix) and the minimum class certainty are used to ensure the high confidence of selected candidate set samples, and then the most informative samples are identified for classification from the candidate samples. To save computing costs, we adopt clustering method to reduce the self-relearning samples. Based on our experimental results, the self-relearning algorithm increases the final classification accuracy by approximately 2.5% (from 92.64% to 95.09%) in the case of using few initial training samples, providing a feasible solution for the multi-date change detection.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Xiaoyu Chang, Xin Huang, and Jiayi Li "A novel self-relearning approach for Landsat image change detection", Proc. SPIE 11428, MIPPR 2019: Multispectral Image Acquisition, Processing, and Analysis, 114280J (14 February 2020); https://doi.org/10.1117/12.2538192
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
Earth observing sensors
Landsat
Detection and tracking algorithms
Soil science
Image classification
Remote sensing
Environmental monitoring
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top