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Quantifying System-Level Performance Bottlenecks in Hyperspectral Target Detection — arXiv:2611.08927

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08927
License (as archived): CC0-1.0
Archived: 2026-11-09T23:04:29.397Z
SHA-256 of the archived text: 7531d990924e758af04368432bcf941a2600638dbca3e617c9649c9d7963b170
Stable URL of this page: https://metraipolis.com/documento/dc_5563fbb80ee8

How to cite

Quantifying System-Level Performance Bottlenecks in Hyperspectral Target Detection — arXiv:2611.08927 — arXiv (en), https://arxiv.org/abs/2611.08927 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5563fbb80ee8 (Metraipolis archive dc_5563fbb80ee8, sha256 7531d990924e758af04368432bcf941a2600638dbca3e617c9649c9d7963b170)

BibTeX

@misc{dc5563fbb80ee8,
  title = {Quantifying System-Level Performance Bottlenecks in Hyperspectral Target Detection — arXiv:2611.08927},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08927},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5563fbb80ee8; sha256 7531d990924e758af04368432bcf941a2600638dbca3e617c9649c9d7963b170},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08927. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Discerning core performance bottlenecks in hyperspectral target detection is highly challenging given the complex interplay between sub-systems of the imaging chain (scene, atmosphere, sensor and processing). Prior sensitivity and limitation studies offer narrow insights applicable to specific system configurations. To address the need for broader generalization, an analysis framework was constructed to quantify limitations from large ensembles of model scenarios. Key components of the framework include the use of canonical scene representations and the log-weighted area under the ROC curve (wAUC), a metric well-suited for rare target detection. By analyzing thousands of scenarios, parametric thresholds were quantified where detection performance degrades, corresponding to "knees" in performance curves. A knee significance score (KSS) is introduced to assess the relative importance of these thresholds. The framework an airborne VNIR sensor implanted in 28,000 scenarios using seven system parameters spanning the scene, atmosphere and sensor. Results suggest that aerosol visibility in the 2-6 km range is the dominant limiter in detection, followed by subpixel target percentages of 15-35%, and background clutter modeled using a t-distribution with 3-5 degrees of freedom.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv eess.IV, nuovi lavori del giorno).
Categorie arXiv: Image and Video Processing (eess.IV).
Pagina del lavoro: https://arxiv.org/abs/2611.08927 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-09T22:59:37.171Z dalla pagina https://arxiv.org/list/eess.IV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).