.png){kind=icon}
.png){kind=icon}
Return
Unsupervised Multitemporal Triclass Change Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2024.3442156.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Change detection is a fundamental task that involves assessing changes in a given region over multiple time periods. It has been widely applied across various fields, including monitoring deforestation, urban expansion, and natural disaster analysis. In this article, we address the critical and complex issue of automatically identifying types of changes in land cover using remotely sensed imagery. While conventional unsupervised change detection methods typically focus on comparing pairs of images and making a binary decision between change and nonchange, our approach tackles the challenge of analyzing long image series and identifying the kind of change. Under this condition, the unsupervised change detection process allows for a more informative identification of the land cover dynamics. Moreover, our approach transforms input data to a new representation, capturing the target's spectral response changes over time. Through the utilization of stochastic distances and an optimized thresholding scheme, areas exhibiting minimal spectral response variance are classified as unchanged, effectively distinguishing them from regions undergoing modifications. Next, by applying autocorrelation analysis, regions exhibiting temporal modifications are segregated into periodic (i.e., seasonal) and aperiodic (i.e., permanent) change cases. Experimental validation using both simulated and real-world remote sensing image series demonstrates the effectiveness of the proposed approach.
Keywords:
Feature extraction
Remote sensing
Stochastic processes
Spatiotemporal phenomena
Radar polarimetry
Land surface
Kernel
Actual data
classification
feature extraction
simulated data
statistical modeling
time series
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
View more organizations