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

arXiv: https://arxiv.org/abs/2609.31074 · HTML: https://mlsanity.com/paper/2609.31074/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 25 Sep 2026.

## Builds on (most cited first, 7 of 7)

- [Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image](https://mlsanity.com/paper/2012.10122/) (2020)
- [Hyperspectral Imaging-Based Perception in Autonomous Driving Scenarios: Benchmarking Baseline Semantic Segmentation Models](https://mlsanity.com/paper/2410.22101/) (2024)
- [Hyperspectral Sensors and Autonomous Driving: Technologies, Limitations, and Opportunities](https://mlsanity.com/paper/2508.19905/) (2025)
- [CSNR and JMIM Based Spectral Band Selection for Reducing Metamerism in Urban Driving](https://mlsanity.com/paper/2508.10962/) (2025)
- [Hyperspectral vs. RGB for Pedestrian Segmentation in Urban Driving Scenes: A Comparative Study](https://mlsanity.com/paper/2508.11301/) (2025)
- [Supervised Embedded Methods for Hyperspectral Band Selection](https://mlsanity.com/paper/2401.11420/) (2024)
- [Learnable Quantum Efficiency Filters for Urban Hyperspectral Segmentation](https://mlsanity.com/paper/2603.26528/) (2026)

## Abstract (arXiv)

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∈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.