Hyperspectral Target Detection : An Overview of Current and Future Challenges

Nasrabadi, Nasser M.

doi:10.1109/msp.2013.2278992

Cited by 493 publications

(205 citation statements)

References 33 publications

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“…Essentially, target/anomaly detection consists in a binary classification technique that labels every pixel in the hyperspectral cube as belonging to target/background or outlier/background, respectively [70]. Due to some nuances establishing the specific goal of finding a target/anomaly among data through the classification of two classes, this detection approach was detached from the classification methods that will be addressed after this section.…”

Section: Target and Anomaly Detectionmentioning

confidence: 99%

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Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

et al. 2017

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Traditional imagery-provided, for example, by RGB and/or NIR sensors-has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre-and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry-wherein the combination of UAV and hyperspectral sensors plays a center role-is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing-by isolating the common user from the processes' mathematical complexity-several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions.

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Section: Target and Anomaly Detectionmentioning

confidence: 99%

“…Meanwhile, other developments were made by Nasrabadi [70], who addressed anomaly and target detection with recent statistical signal processing and machine learning approaches. In spite of its experimental results being around full-pixel targets, he claimed that the addressed methods can be extended to sub-pixel.…”

Section: Subpixel Target Detectionmentioning

confidence: 99%

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

et al. 2017

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“…Signal sources appear as anomalies in the data, such as unexpected presence, low probability of occurrence, small sample population whose signature is spectrally distinct from spectral signatures of its surrounding data samples. As a result, anomaly detection has received considerable interest in hyperspectral imaging in the last twenty years [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows

et al. 2018

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Abstract:Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. This paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. A causal sample covariance/correlation matrix is derived for local window background. As for the real-time sliding windows, the Woodbury identity is used in recursive update equations, which could avoid the calculation of historical information and thus speed up the processing. Furthermore, a background suppression algorithm is also proposed in this paper, which removes the current under test pixel from the recursively update processing. Experiments are implemented on a real hyperspectral image. The experiment results demonstrate that the proposed anomaly detector outperforms the traditional real-time local background detector and has a significant speed-up effect on calculation time compared with the traditional detectors.

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“…In assistant with remote sensing and spectral imaging technology, camouflage target detection becomes an effective tool for many applications so as to discover aircraft wreckage, oil leakage, drug vegetation, camouflage military targets in a secure and rapid way [2]. Precise target recognition inevitably relies on the support from geometric, spectral or even polarization characteristics of the target.…”

Section: Introductionmentioning

confidence: 99%

Research on Camouflage Target Detection via Computational Analysis with Spectral Imaging

Chen¹,

Zhou²,

Chen³

et al. 2018

dtcse

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Camouflage target detection is one of most important techniques in a great number of applications such as remote sensing, security monitoring and industrial production. In this paper, high-resolution spectral imaging together with computational analysis on spectral data cube is adopted to solve camouflage target recognition problem. Spectral data cubes of certain experimental scene are collected by a self-developed visual-band imaging spectrometer, and spectral information divergence (SID) is adopted as difference descriptor to discriminate abnormal target. SIDs of the whole spectral region and several specific ranges are all measured to quantitatively evaluate the effort of band selection on camouflage detection. This is proven to be a low-cost and effective tool for camouflage target detection and can be further developed beyond visual band spectral imaging technique.

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Hyperspectral Target Detection : An Overview of Current and Future Challenges

Cited by 493 publications

References 33 publications

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows

Research on Camouflage Target Detection via Computational Analysis with Spectral Imaging

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