Deep learning-based spectral band selection for hyperspectral imaging tasks

Hyperspectral imaging (HSI) captures measurements at multiple wavelengths across the electromagnetic spectrum, providing information that improves material segmentation and classification beyond RGB imagery. While HSI devices often acquire a large number of spectral bands, this increases both cost and acquisition time. However, not all bands contribute equally to task-specific performance. We propose a deep spectral band selection (DSBS) framework for HSI tasks. Unlike methods that preserve non-task-specific information, DSBS identifies the most informative bands for a given task by jointly training a fully differentiable band selector and a neural network in an end-to-end (E2E) learning scheme. The selection is guided by a bin function and an ℓ p -norm regularizer to reach a target number of bands. Experiments on segmentation and classification show that DSBS outperforms state-of-the-art machine learning and deep learning methods. Results show that DSBS outperforms state-of-the-art E2E methods for HSI classification by about 8% across the evaluated metrics and yields an improvement of 1% over using all spectral bands for material segmentation. Additionally, we validate DSBS in a testbed implementation, starting from full-spectrum images (301 bands). The end-to-end training converges to 10 wavelengths; under cross-validation, considering only these 10 bands yields competitive cocoa-bean classification, with an overall accuracy of 76.40%, retaining approximately 95.5% of the accuracy observed in the 10-band end-to-end evaluation (80.01%) while reducing acquisition speed by a factor of 30 times.