Embedded Hyperspectral Band Selection with Adaptive Optimization for Image Semantic Segmentation

• URL: http://arxiv.org/abs/2401.11420v1
• Date: Sun, 21 Jan 2024 07:48:39 GMT
• Title: Embedded Hyperspectral Band Selection with Adaptive Optimization for Image Semantic Segmentation
• Authors: Yaniv Zimmer and Oren Glickman
• Abstract summary: We introduce a pioneering approach for hyperspectral band selection that offers an embedded solution. Our proposed method, embedded Hyperspectral Band Selection (EHBS), excels in selecting the best bands without the need for prior processing. The adaptability of our approach to other tasks opens up promising avenues for broader applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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