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Spectral Transformations for Multitemporal Hyperspectral Classification
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AI
DOI：10.1109/LGRS.2021.3136569.png)
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Abstract
En 中文
With the impending launch of numerous hyperspectral satellites, the number of multitemporal datasets will exponentially increase, greatly increasing our need to develop algorithms designed to handle this imagery. Classifying scenes over time is challenging due to factors such as illumination changes due to the time of year, vegetation phenology, and changes in phenology patterns due to differences in precipitation and temperature. In order to improve multitemporal classifications, this letter investigates multiple algorithms that transform training data with no labels to classify future scenes. Four classes of algorithms are investigated to execute the transformations: linear, affine, decision tree, and random forest (L, A, D, and R). Experiments were conducted with hyperspectral airborne data collected three times per year for three consecutive years over Southern California. Classification accuracies with and without the transformations were compared. The mean improvements in accuracy achieved using the transformations were significant: 38%, 38%, 20%, and 22% for the four algorithm classes, (L, A, D, and R), respectively. These transformation algorithms present an opportunity for improving multitemporal classifications, even for challenging scenes, with simple calculations that are less computationally complex than existing algorithms. This approach represents another step to fully leveraging the unique and powerful temporal capabilities that will become available soon through upcoming or recently launched hyperspectral satellites.
Keywords:
Airborne visible/infrared imaging spectrometer (AVIRIS)
hyperspectral
multiple dates
multitemporal
phenology
Journal
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5.1K
