# Validating Hyperspectral Image Segmentation

arXiv: https://arxiv.org/abs/1811.03707 · HTML: https://mlsanity.com/paper/1811.03707/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2018.

## Built on by (most cited first, 18 of 18)

- [Leakage and the Reproducibility Crisis in ML-based Science](https://mlsanity.com/paper/2207.07048/) (2022)
- [A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging](https://mlsanity.com/paper/2106.15944/) (2021)
- [Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines](https://mlsanity.com/paper/2302.08270/) (2023)
- [Reproducibility in Machine Learning-Driven Research](https://mlsanity.com/paper/2307.10320/) (2023)
- [Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images](https://mlsanity.com/paper/2108.05547/) (2021)
- [SpectralWaste Dataset: Multimodal Data for Waste Sorting Automation](https://mlsanity.com/paper/2403.18033/) (2024)
- [Deep Diversity-Enhanced Feature Representation of Hyperspectral Images](https://mlsanity.com/paper/2301.06132/) (2023)
- [Triplet-Watershed for Hyperspectral Image Classification](https://mlsanity.com/paper/2103.09384/) (2021)
- [Physics-Informed Spectral Modeling for Hyperspectral Imaging](https://mlsanity.com/paper/2508.21618/) (2025)
- [Audio scene monitoring using redundant ad-hoc microphone array networks](https://mlsanity.com/paper/2103.01830/) (2021)
- [Testing the Consistency of Performance Scores Reported for Binary Classification Problems](https://mlsanity.com/paper/2310.12527/) (2023)
- [On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging](https://mlsanity.com/paper/2203.11209/) (2022)
- [Objective Evaluation-based High-efficiency Learning Framework for Hyperspectral Image Classification](https://mlsanity.com/paper/2302.05297/) (2023)
- [AMBER -- Advanced SegFormer for Multi-Band Image Segmentation: an application to Hyperspectral Imaging](https://mlsanity.com/paper/2409.09386/) (2024)
- [Enhancing Knowledge Transfer in Hyperspectral Image Classification via Cross-scene Knowledge Integration](https://mlsanity.com/paper/2512.08989/) (2025)
- [Data Leakage Inflates Generalizability of Power Outage Prediction Models](https://mlsanity.com/paper/2608.24665/) (2026)
- [Ten Architectures, One Error: Shared Failure Modes in Hyperspectral Classification under Spatially Disjoint Evaluation](https://mlsanity.com/paper/2609.01786/) (2026)
- [Hyperspectral Image Models: Technical Report](https://mlsanity.com/paper/2609.39871/) (2026)

## Abstract (arXiv)

Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, hence automated approaches for precise segmentation of such imagery are being rapidly developed. In this letter, we share our observations on the strategy for validating hyperspectral image segmentation algorithms currently followed in the literature, and show that it can lead to over-optimistic experimental insights. We introduce a new routine for generating segmentation benchmarks, and use it to elaborate ready-to-use hyperspectral training-test data partitions. They can be utilized for fair validation of new and existing algorithms without any training-test data leakage.