mlsanityData to 7 Oct 2026

Validating Hyperspectral Image Segmentation

arXiv 1811.03707 ↗First posted 8 Nov 2018cs.CV cs.LGcited by 18

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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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Nov 2018.

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Citing papers per year: 2021: 4; 2022: 2; 2023: 5; 2024: 2; 2025: 2; 2026: 3.

18 papers.

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  1. 2022Leakage and the Reproducibility Crisis in ML-based Sciencecited by 63
  2. 2021A survey on computational spectral reconstruction methods from RGB to hyperspectral imagingcited by 20
  3. 2023Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machinescited by 15
  4. 2023Reproducibility in Machine Learning-Driven Researchcited by 15
  5. 2021Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Imagescited by 7
  6. 2024SpectralWaste Dataset: Multimodal Data for Waste Sorting Automationcited by 6
  7. 2023Deep Diversity-Enhanced Feature Representation of Hyperspectral Imagescited by 5
  8. 2025Physics-Informed Spectral Modeling for Hyperspectral Imagingcited by 2
  9. 2021Audio scene monitoring using redundant ad-hoc microphone array networkscited by 1
  10. 2021Triplet-Watershed for Hyperspectral Image Classificationcited by 1
  11. 2023Testing the Consistency of Performance Scores Reported for Binary Classification Problemscited by 1
  12. 2022On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imagingcited by 0
6 more
  1. 2023Objective Evaluation-based High-efficiency Learning Framework for Hyperspectral Image Classificationcited by 0
  2. 2024AMBER -- Advanced SegFormer for Multi-Band Image Segmentation: an application to Hyperspectral Imagingcited by 0
  3. 2025Enhancing Knowledge Transfer in Hyperspectral Image Classification via Cross-scene Knowledge Integrationcited by 0
  4. 2026Data Leakage Inflates Generalizability of Power Outage Prediction Modelscited by 0
  5. 2026Ten Architectures, One Error: Shared Failure Modes in Hyperspectral Classification under Spatially Disjoint Evaluationcited by 0
  6. 2026Hyperspectral Image Models: Technical Reportcited by 0