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Hyperspectral chemical plume detection algorithms based on multidimensional iterative filtering decomposition.

A Cicone1, J Liu2, H Zhou2

  • 1INdAM, DISIM, Università degli Studi dell'Aquila, via Vetoio 1, L'Aquila 67100, Italy antonio.cicone@univaq.it.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|March 9, 2016
PubMed
Summary

Identifying airborne chemical plumes from hyperspectral images is challenging. This study introduces a novel multidimensional iterative filtering (MIF) algorithm to improve plume boundary detection and proposes a pre-processing method enhancing cosine similarity for accurate chemical identification.

Keywords:
empirical mode decompositioniterative filteringthreat detection

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Area of Science:

  • Remote Sensing
  • Environmental Monitoring
  • Signal Processing

Background:

  • Airborne chemical releases pose significant risks to human health and the environment.
  • Hyperspectral imaging offers a potential method for detecting chemical plumes, but accurate identification is often hindered by noise and sensor limitations.
  • Standard techniques like matched filtering can struggle with precise plume boundary delineation.

Purpose of the Study:

  • To develop advanced methods for improving the accuracy of chemical plume identification and boundary detection in hyperspectral imagery.
  • To introduce a novel post-processing tool and a pre-processing technique to enhance existing classification algorithms.
  • To demonstrate the effectiveness of these methods on real-world hyperspectral datasets.

Main Methods:

  • The study utilizes the multidimensional iterative filtering (MIF) algorithm, a non-stationary signal decomposition technique, for post-processing hyperspectral data.
  • A pre-processing method based on MIF is proposed to decorrelate and mean-center hyperspectral datasets.
  • The performance of the cosine similarity measure is evaluated after applying the proposed pre-processing technique.

Main Results:

  • The multidimensional iterative filtering (MIF) algorithm significantly improves the performance of classification methods in identifying chemical plume boundaries.
  • The proposed pre-processing method enhances the effectiveness of the cosine similarity measure, transforming it into a high-performing classifier.
  • The combined pre- and post-processing techniques demonstrate superior results in accurately identifying chemical pixels and plume edges.

Conclusions:

  • The developed MIF-based pre-processing and post-processing techniques offer a robust solution for enhancing chemical plume detection in hyperspectral imagery.
  • These adaptive, data-driven methods overcome limitations of traditional techniques, leading to more accurate environmental monitoring.
  • The findings have practical implications for real-world applications requiring precise identification of hazardous airborne chemicals.