Multiscale Superpixel Guided Discriminative Forest for Hyperspectral Anomaly Detection

Recently, the isolation forest (IF) methods have received increasing attention for their promising performance in hyperspectral anomaly detection (HAD). However, limited by the ability of exploiting spatial-spectral information, existing IF-based methods suffer from a lot of false alarms and disappo...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Remote sensing (Basel, Switzerland) Switzerland), 2022-10, Vol.14 (19), p.4828
Hauptverfasser: Cheng, Xi, Zhang, Min, Lin, Sheng, Zhou, Kexue, Wang, Liang, Wang, Hai
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Recently, the isolation forest (IF) methods have received increasing attention for their promising performance in hyperspectral anomaly detection (HAD). However, limited by the ability of exploiting spatial-spectral information, existing IF-based methods suffer from a lot of false alarms and disappointing performance of detecting local anomalies. To overcome the two problems, a multiscale superpixel guided discriminative forest method is proposed for HAD. First, the multiscale superpixel segmentation is employed to generate some homogeneous regions, and it can effectively extract spatial information to guide anomaly detection for the discriminative forest in local areas. Then, a novel discriminative forest (DF) model with the gain split criterion is designed, which enhances the sensitivity of the DF to local anomalies by the utilization of multi-dimension spectral bands for node division; meanwhile, the acceptable range of hyperplane attribute values is introduced to capture any unseen anomaly pixels that are out-of-range in the evaluation stage. Finally, for the high false alarm rate situation in the existing IF-based algorithms, the multiscale fusion with guided filtering is put forward to refine the initial detection results from the DF. In addition, the extensive experimental results on four real hyperspectral datasets demonstrate the effectiveness of the proposed method.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14194828