Embedded Hyperspectral Band Selection with Adaptive Optimization for Image Semantic Segmentation

2024-01-21

arXiv.org

AI Summary

Hyperspectral image (HSI) semantic segmentation suffers from spectral redundancy and high computational cost, while existing band selection methods often rely on preprocessing and are decoupled from downstream tasks. To address this, we propose Embedded Hyperspectral Band Selection (EHBS), the first end-to-end trainable framework that integrates band selection directly into semantic segmentation training. EHBS introduces a differentiable stochastic band gating mechanism for dynamic spectral filtering, employs a differentiable ℓ₀-norm regularization to precisely control band sparsity, and incorporates a parameter-free dynamic optimizer (DoG) that adaptively adjusts learning rates. Evaluated on two mainstream HSI segmentation benchmarks, EHBS achieves state-of-the-art performance—delivering higher accuracy and significantly reduced model complexity—while demonstrating strong generalization to grouped feature selection tasks.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Dimensionality Reduction/Feature Selection
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• Reducing high dimensionality of hyperspectral imaging data
• Integrating band selection directly with downstream tasks
• Eliminating separate preprocessing steps in selection methods

Innovation

• Embedding band selection within deep learning models
• Eliminating separate preprocessing steps for efficiency
• Ensuring alignment with target task performance