Recursive classification of satellite imaging time-series: An application to land cover mapping
Despite the extensive body of literature focused on remote sensing applications for land cover mapping and the availability of high-resolution satellite imagery, methods for continuously updating classification maps in real-time remain limited, especially when training data is scarce. This paper int...
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Main Authors | , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
04.01.2023
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Online Access | Get full text |
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Summary: | Despite the extensive body of literature focused on remote sensing
applications for land cover mapping and the availability of high-resolution
satellite imagery, methods for continuously updating classification maps in
real-time remain limited, especially when training data is scarce. This paper
introduces the Recursive Bayesian Classifier (RBC), which converts any
instantaneous classifier into a robust online method through a probabilistic
framework that is resilient to non-informative image variations. Three
experiments are conducted using Sentinel-2 data: water mapping of the Oroville
Dam in California and the Charles River basin in Massachusetts, and
deforestation detection in the Amazon. RBC is applied to a Gaussian Mixture
Model (GMM), Logistic Regression (LR), and our proposed Spectral Index
Classifier (SIC). Results show that RBC significantly enhances classifier
robustness in multitemporal settings under challenging conditions, such as
cloud cover and cyanobacterial blooms. Specifically, balanced classification
accuracy improves by up to 26.95% for SIC, 12.4% for GMM, and 13.81% for LR in
water mapping, and by 15.25%, 14.17%, and 14.7% in deforestation detection.
Moreover, without additional training data, RBC improves the performance of the
state-of-the-art DeepWaterMap and WatNet algorithms by up to 9.62% and 11.03%.
These benefits are provided by RBC while requiring minimal supervision and
maintaining a low computational cost that remains constant for each time step
regardless of the time-series length. |
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DOI: | 10.48550/arxiv.2301.01796 |