Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/57514
Title: | A Line-by-Line Fast Anomaly Detector for Hyperspectral Imagery | Authors: | Díaz Martín, María Guerra Hernández, Raúl Celestino Horstrand, Pablo López Suárez, Sebastián Sarmiento Rodríguez, Roberto |
UNESCO Clasification: | 3307 Tecnología electrónica | Keywords: | Anomaly detection (AD) Hyperspectral imagery Line-by-Line Fast Anomaly Detector for Hyperspectral Imagery (LbL-FAD) algorithm Line-by-line processing Orthogonal projections, et al |
Issue Date: | 2019 | Project: | European Initiative to Enable Validation for Highly Automated Safe and Secure Systems Iniciativa Europea Para Facilitar la Validacion de Sistemas Seguros y Altamente Automatizados Plataforma Hw/Sw Distribuida Para El Procesamiento Inteligente de Información Sensorial Heterogenea en Aplicaciones de Supervisión de Grandes Espacios Naturales |
Journal: | IEEE Transactions on Geoscience and Remote Sensing | Abstract: | In recent years, anomaly detection (AD) has enjoyed a growing interest in hyperspectral data analysis. However, most state-of-the-art detectors need to work with the entire hyperspectral cube, what prevents their use for applications under real-time constraints, especially when the hyperspectral data are collected by push-broom scanners that acquire the hyperspectral images (HSIs) in a line-by-line fashion. In this paper, a Line-by-Line Fast Anomaly Detector for Hyperspectral Imagery (LbL-FAD) is proposed, which is capable of processing each sensed line as soon as it is captured. The LbL-FAD works under the assumption that anomalous pixels cannot be well represented by the background distribution. It uses an orthogonal projection strategy for extracting a set of pixels from the first captured hyperspectral frames, i.e., lines of pixels, that are used for representing the background distribution. Using these pixels, the LbL-FAD proposes a hardware-friendly alternative to compute the orthogonal subspace to that spanned by the selected background samples, making the anomalous pixels better detectable. In addition, the LbL-FAD incorporates an automatic thresholding method which provides line-by-line and real-time binary maps where anomalous targets are segmented from the background. This novelty clearly differentiates the proposed LbL-FAD from the conventional anomaly detectors, which usually are not able to automatically discriminate anomalous pixels from background pixels until the entire image is processed. Several experiments have been carried out using real HSIs collected by different sensors. The obtained results clearly support the benefits of our proposal, both in terms of the accuracy of the detection performance and the computational complexity. | URI: | http://hdl.handle.net/10553/57514 | ISSN: | 0196-2892 | DOI: | 10.1109/TGRS.2019.2923921 | Source: | IEEE Transactions on Geoscience and Remote Sensing [ISSN 0196-2892], v. 57(11), p. 8968-8982 |
Appears in Collections: | Artículos |
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.