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\begin{document}

\title{Deep Interactive Annotation to Support Hyperspectral Image Analysis}



\author[1]{Xiaolei Guo}%
\author[1]{Alina Zare}%
\affil[1]{University of Florida}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\begin{abstract}
Annotation plays an essential and important role in~the performance of
machine learning approaches. However, manually labeling objects
accurately is often time-consuming, especially when processing large
image data. To improve this process, we developed a deep learning-based
interactive annotation approach with application to hyperspectral
imagery. Specifically, a deep network with a metric-based loss function
will be pre-trained on a large training set. Encoding layers in the
network are used for dimensionality reduction as well as boosting
discriminability among pixels belonging to different classes in the
embedding space. Then, when new images are provided, the user can
provide (fast, easy, weak) annotations to indicate objects of interest
and fine-tune the pre-trained network to the task at hand. In this way,
the fine-tuned network can adapt for the new category and task.
Annotations can be iteratively modified and corrected. Once the model is
fine-tuned by quickly annotating a few images, it can then be
generalized to a large image dataset of similar objects. Experiments on
the MUUFL Gulfport hyperspectral imaging dataset show that our approach
outperforms classical active learning methods based on support vector
machine in average Intersect of Union (from around 65\% to around 80\%)
with significant improvement on computing speeds (from 30-200 seconds to
less than 10 seconds)%
\end{abstract}%



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