Your browser doesn't support javascript.
loading
Greedy Ensemble Hyperspectral Anomaly Detection.
Hossain, Mazharul; Younis, Mohammed; Robinson, Aaron; Wang, Lan; Preza, Chrysanthe.
Afiliação
  • Hossain M; Computer Science Department, The University of Memphis, Memphis, TN 38152, USA.
  • Younis M; Electrical and Computer Engineering Department, The University of Memphis, Memphis, TN 38152, USA.
  • Robinson A; Electrical and Computer Engineering Department, The University of Memphis, Memphis, TN 38152, USA.
  • Wang L; Computer Science Department, The University of Memphis, Memphis, TN 38152, USA.
  • Preza C; Electrical and Computer Engineering Department, The University of Memphis, Memphis, TN 38152, USA.
J Imaging ; 10(6)2024 May 28.
Article em En | MEDLINE | ID: mdl-38921608
ABSTRACT
Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport-Beach-Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article