Embedded Hyperspectral Band Selection with Adaptive Optimization for Image Semantic Segmentation

Abstract

The selection of hyperspectral bands plays a pivotal role in remote sensing and image analysis, with the aim of identifying the most informative spectral bands while minimizing computational overhead. This paper introduces 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 needing prior processing, seamlessly integrating with the downstream task model. This is achieved through stochastic band gates along with an approximation of the l0 norm on the number of selected bands as the regularization term and the integration of a dynamic optimizer, DoG, which removes the need for the required tuning of the learning rate. We conduct experiments on two distinct semantic-segmentation hyperspectral benchmark datasets, demonstrating their superiority in terms of accuracy and ease of use compared to many common and state-of-the-art methods. Furthermore, our contributions extend beyond hyperspectral band selection. Our approach's adaptability to other tasks, especially those involving grouped features, opens promising avenues for broader applications within the realm of deep learning, such as feature selection for feature groups.

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Reproductions