  Hyperspectral band selection plays a pivotal role in remote sensing and image
analysis, aiming to identify the most informative spectral bands while
minimizing computational overhead. In this paper, we introduce a pioneering
approach for hyperspectral band selection that offers an embedded solution,
making it well-suited for resource-constrained or real-time applications. Our
proposed method, embedded Hyperspectral Band Selection (EHBS), excels in
selecting the best bands without the need for prior processing, seamlessly
integrating with the downstream task model. This is achieved through the
adaptation of the Stochastic Gates (STG) algorithm, originally designed for
feature selection, for hyperspectral band selection in the context of image
semantic segmentation and the integration of a dynamic optimizer, DoG, which
removes the need for the required tuning the learning rate. To assess the
performance of our method, we introduce a novel metric for evaluating band
selection methods across different target numbers of selected bands quantified
by the Area Under the Curve (AUC). We conduct experiments on two distinct
semantic-segmentation hyperspectral benchmark datasets, demonstrating its
superiority in terms of its resulting accuracy and its ease of use compared to
many common and state-of-the-art methods. Furthermore, our contributions extend
beyond the realm of hyperspectral band selection. The adaptability of our
approach to other tasks, especially those involving grouped features, opens up
promising avenues for broader applications within the realm of deep learning,
such as feature selection for feature groups. The demonstrated success on the
tested datasets and the potential for application to a variety of tasks
underscore the value of our method as a substantial addition to the field of
computer vision.
