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
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]
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