Band-Selection Stability and Semantic Segmentation Performance: A Study on Hyperspectral City

2026-09-25

AI Summary

This study addresses the sampling sensitivity of hyperspectral band selection and its unclear relationship with downstream segmentation performance. We evaluate the stability of six methods using Jaccard similarity and conduct Top-K band selection combined with algorithms such as Sim-LP on the Hyperspectral City dataset, benchmarked across three semantic segmentation models. Our analysis reveals that intra-method stability is informative yet not a reliable performance indicator, underscoring the necessity of comprehensive evaluation across samples, subsets, and models. Ultimately, the proposed approach achieves up to a 1.72 mF1 improvement alongside an 18–22× inference acceleration. These results demonstrate that efficient and competitive hyperspectral perception is attainable even with a limited number of spectral bands.

Abstract

Resource constraints make high-dimensional hyperspectral imaging challenging in autonomous perception, motivating the use of band selection methods. However, the sensitivity of band-selection methods to sampled data and their relationship to semantic segmentation models (SSMs) remain underexplored. This study evaluates six band selection methods on ten independently sampled, class-balanced region-of-interest (ROI) sets, yielding 60 top-25 band subsets from the Hyperspectral City V2 (128 bands: 450-950nm) dataset. Top-$K$ bands ($K\in\{3,5, ... 13\}$) from the first three ROI sets are evaluated with three SSMs against the corresponding 128-band baseline. Experiments show that intra-method stability is method-dependent: Sim-LP shows the highest stability (pairwise Jaccard similarity) and, together with JMIM+CSNR, yields the best segmentation results. Top-$K$ based SSMs remain competitive with baselines, with gains of up to 2.01 mIoU and 1.72 mF1 points, and 18-22x faster CPU inference for $K=9$. However, performance does not improve monotonically with $K$, and stability shows no consistent association with SSM performance. These findings suggest that intra-method stability is informative but an unreliable indicator of downstream segmentation performance, highlighting the need to evaluate band-selection methods across repeated samples, subset sizes, and SSMs.

Problem

• band selection
• hyperspectral imaging
• semantic segmentation
• stability
• autonomous perception

Innovation

• Band Selection
• Semantic Segmentation
• Hyperspectral Imaging
• Stability Analysis
• Autonomous Perception