Automated Hyperspectral Cueing for Civilian Search and Rescue

Eismann, Michael T.; Stocker, Alan D.; Nasrabadi, Nasser M.

doi:10.1109/jproc.2009.2013561

Cited by 209 publications

(119 citation statements)

References 87 publications

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“…Bue et al (2015) calculated reflectance products using the model-based ATREM atmospheric correction. Eismann et al (2009) demonstrated the ARCHER system which provided real-time processing for search and rescue applications. They performed matched filter spectral signature detection and change detection using the chronochrome method (Schaum and Stocker, 1998).…”

Section: Tactical Imaging Spectroscopymentioning

confidence: 99%

Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

et al. 2015

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Abstract. Localized anthropogenic sources of atmospheric CH 4 are highly uncertain and temporally variable. Airborne remote measurement is an effective method to detect and quantify these emissions. In a campaign context, the science yield can be dramatically increased by real-time retrievals that allow operators to coordinate multiple measurements of the most active areas. This can improve science outcomes for both single-and multiple-platform missions. We describe a case study of the NASA/ESA CO 2 and MEthane eXperiment (COMEX) campaign in California during June and August/September 2014. COMEX was a multi-platform campaign to measure CH 4 plumes released from anthropogenic sources including oil and gas infrastructure. We discuss principles for real-time spectral signature detection and measurement, and report performance on the NASA Next Generation Airborne Visible Infrared Spectrometer (AVIRIS-NG). AVIRIS-NG successfully detected CH 4 plumes in realtime at Gb s −1 data rates, characterizing fugitive releases in concert with other in situ and remote instruments. The teams used these real-time CH 4 detections to coordinate measurements across multiple platforms, including airborne in situ, airborne non-imaging remote sensing, and groundbased in situ instruments. To our knowledge this is the first reported use of real-time trace-gas signature detection in an airborne science campaign, and presages many future applications. Post-analysis demonstrates matched filter methods providing noise-equivalent (1σ ) detection sensitivity for 1.0 % CH4 column enhancements equal to 141 ppm m.

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Section: Tactical Imaging Spectroscopymentioning

confidence: 99%

Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

et al. 2015

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“…Target detection aims to discriminate the specific target pixels from various of backgrounds, which is one of the most significant applications in HSI [6,7] processing. In most cases, the a priori information about the spectrum of the target of interest is difficult to obtain, so the target detection technique with no a priori information, known as anomaly detection, is widely used in practical situations [4,8,9].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Background Estimation and Adaptive Weighted Sparse Representation

Zhu

Wen

2018

Remote Sensing

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Abstract:Anomaly detection is an important task in hyperspectral imagery (HSI) processing. It provides a new way to find targets that have significant spectral differences from the majority of the dataset. Recently, the representation-based methods have been proposed for detecting anomaly targets in HSIs. It is essential for this type of method to construct a valid background dictionary to distinguish anomaly and background accurately. In this paper, a novel hyperspectral anomaly detection method based on background estimation and adaptive weighted sparse representation has been proposed. Firstly, to obtain the effective background dictionary without anomaly information, a new background dictionary construction strategy is designed. Secondly, the sparse representation based on the constructed background dictionary is utilized on the dataset. Anomalies and background are distinguished through the response of the residual matrix. Thirdly, the residual matrix is weighted adaptively from global and local domains, which makes anomalies and background more discriminative. An important advantage of the proposed method is that it considers the properties of anomalies in both spectral and spatial domains. Experiments on three HSI datasets reveal that our proposed method achieves an outstanding detection performance compared with the other anomaly detection algorithms.

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“…HSI consists of a number of bands even up to several hundreds over the spectrum, which includes a wealth of information, thus has been widely applied for many practical applications such as the management of the environment, precision agriculture, social security and military defense as well as mineralogy [1][2][3]. One of the most important applications of HSI is image classification.…”

Section: Introductionmentioning

confidence: 99%

Recursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification

Zhang

Liang

et al. 2017

IEEE Geosci. Remote Sensing Lett.

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Automated Hyperspectral Cueing for Civilian Search and Rescue

Cited by 209 publications

References 87 publications

Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

Hyperspectral Anomaly Detection via Background Estimation and Adaptive Weighted Sparse Representation

Recursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification

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