A hyperspectral anomaly detection algorithm based on orthogonal subspace projection

Liu, Ying; Gao, Kun; Wang, Lijing; Zhuang, Youwen

doi:10.1117/12.2072616

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…The subspace-based anomaly detection algorithm considers that anomaly and background pixels have a greater degree of separation in a suitable subspace. According to its implementation, subspace-based anomaly detection can be divided into orthogonal subspace-based [23] and other subspace-based approaches [24]. For instance, the orthogonal subspace-based methods project testing pixels into an orthogonal subspace of the background components, where the background and anomalies can be better separated.…”

Section: Metricsmentioning

confidence: 99%

Learning-Free Hyperspectral Anomaly Detection With Unpredictive Frequency Residual Priors

Zhou

Wang

Gao

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral anomaly detection aims to fast and credibly find nontrivial candidate targets without prior knowledge, which has become an increasingly pressing need as imagery swath and resolution are growing rapidly. Relevant state-of-the-art learning-based anomaly detection approaches have benefited from data-driven hierarchical feature embeddings that typically model the geometric distribution of spectral vectors. However, most of these techniques are incompatible with resource-constrained applications: 1) huge computational costs caused by feedforward convolution operation cannot be supported with limited computation resources and storage (e.g., in-orbit processing); 2) detection accuracy relies on large-scale training datasets, which results in labor-expensive imagery collection, pixel-level labeling, and timeconsuming learning procedure. To address these issues, we advocate a learning-free, frequency domain anomaly detection method combined with predictive coding, an intriguing heuristic prior from human vision system. Technically, 1) inherently efficient frequency transformation could be implemented with existing image compression modules (e.g., JPEG or JPEG2000 codec), which improves the utilization of computational resources; 2) predictive coding mechanism is exploited for suppressing frequently occurring information represented in the low-entropy frequency domain, such that the "unpredictive" subject (i.e., anomaly spectrums) can "pop out" with naive residuals. Such heuristic prior incorporated into the computational model can reduce dependence on the large-scale training set. Experiments on real-world hyperspectral datasets confirm the efficacy of our model. Besides, low computation cost of the proposed (fast frequency transformation and analytical solutions) anomaly detector facilitates rather straightforward sliding window verification in high-resolution imagery.

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Section: Metricsmentioning

confidence: 99%

Learning-Free Hyperspectral Anomaly Detection With Unpredictive Frequency Residual Priors

Zhou

Wang

Gao

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…They demonstrates the insufficiency of statistical methods for this end. Li et al (Liu et al, 2014) adopt the use of outlier detection concept to detect small target un hyperspectral images.…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

A Class-Outlier Approach for Environnemental Monitoring Using Uav Hyperspectral Images

Hemissi

Farah²

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In several remote sensing applications, detecting exceptional/irregular regions (i.e, pixels) with respect to the whole dataset homogeneity is regarded as a very interested issue. Currently, this is limited to the pre-processing step aiming to eliminate the cloud or noisy pixels. In this paper, we propose to extend the coverage area and to tackle this issue by regarding the irregular/exceptional pixels as outliers. The main purpose is the adaptation of the class outlier mining concept in order to find abnormal and irregular pixels in hyperspectral images. This should be done taking into account the class labels and the relative uncertainty of collected data. To reach this goal, the Class Outliers: DistanceBased (CODB) algorithm is enhanced to take into account the multivariate high-dimensional data and the concomitant partially available knowledge of our data. This is mainly done by using belief theory and a learnable task-specific similarity measure. To validate our approach, we apply it for vegetation inspection and normality monitoring. For experimental purposes, the Airborne Prism Experiment (APEX) data, set acquired during an APEX flight campaign in June 2011, was used. Moreover, a collection of simulated hyperspectral images and spectral indices, providing a quantitative indicator of vegetation health, were generated for this purpose. The encouraging obtained results can be used to monitor areas where vegetation may be stressed, as a proxy to detect potential drought.

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Sentinel-2 Imagery for Mapping Cork Oak (Quercus suber L.) Distribution in Calabria (Italy): Capabilities and Quantitative Estimation

Modica

Pollino

2018

New Metropolitan Perspectives

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A hyperspectral anomaly detection algorithm based on orthogonal subspace projection

Cited by 4 publications

References 3 publications

Learning-Free Hyperspectral Anomaly Detection With Unpredictive Frequency Residual Priors

Learning-Free Hyperspectral Anomaly Detection With Unpredictive Frequency Residual Priors

A Class-Outlier Approach for Environnemental Monitoring Using Uav Hyperspectral Images

Sentinel-2 Imagery for Mapping Cork Oak (Quercus suber L.) Distribution in Calabria (Italy): Capabilities and Quantitative Estimation

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