Multi-Spectro-Temporal Analysis of Hyperspectral Imagery Based on 3-D Spectral Modeling and Multilinear Algebra

Hemissi, Selim; Farah, Imed Riadh; Ettabaa, Karim Saheb; Solaiman, Basel

doi:10.1109/tgrs.2012.2200486

Cited by 17 publications

(9 citation statements)

References 41 publications

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“…However, the accuracy of the CD performance depends greatly on the accuracy of a singletime image classification result. In [307], a new approach for modeling the temporal variations of the reflectance response as a function of time period and wavelength was developed. A library of known endmembers that depends mainly on the 3-D surface reconstruction quality and similarity measure is used to perform a classification task.…”

Section: Multiple Change Detectionmentioning

confidence: 99%

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

669

266

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Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information, which make a plethora of applications for the analysis of large areas of the Earth surface feasible. However, a huge number of factors, such as high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions, make such data inherently nonlinear and complex, which poses extreme challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide. This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality reduction, resolution enhancement, hyperspectral image denoising and restoration, change detection, and fast computing. For each topic, we provide a synopsis of the state-of-the-art approaches and numerical results for validation and evaluation of different methodologies, followed by a discussion of future challenges and research directions.

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Section: Multiple Change Detectionmentioning

confidence: 99%

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

669

266

View full text Add to dashboard Cite

show abstract

“…The majority of remote sensing techniques are based on the assumption that the spectral signature of objects is persistent and uniform over time, which might not be true. Therefore, a new model called multi-temporal hyperspectral tensor, denoted by spatial rows × spatial column × wavelength × time is proposed in [90] . This model is obtained by combining multiple hyperspectral images obtained at different time instances.…”

Section: Remote Sensingmentioning

confidence: 99%

Tensor-based anomaly detection: An interdisciplinary survey

Fanaee-T

Gama

2016

Knowledge-Based Systems

126

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Traditional spectral-based methods such as PCA are popular for anomaly detection in a variety of problems and domains. However, if data includes tensor (multiway) structure (e.g. space-time-measurements), some meaningful anomalies may remain invisible with these methods. Although tensor-based anomaly detection (TAD) has been applied within a variety of disciplines over the last twenty years, it is not yet recognized as a formal category in anomaly detection. This survey aims to highlight the potential of tensor-based techniques as a novel approach for detection and identification of abnormalities and failures. We survey the interdisciplinary works in which TAD is reported and characterize the learning strategies, methods and applications; extract the important open issues in TAD and provide the corresponding existing solutions according to the state-of-the-art.

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“…In the remote sensing area, tensor modeling has been increasingly utilized for target detection, 21 denoising, 22 dimensionality reduction, 23,24 and classification. [25][26][27][28] We extract low-dimensional spectral-spatial features by Tucker tensor decomposition 29 and generate probability maps using SVM to indicate how likely it is that each pixel is cancerous. The classification method is generic, which can be applied not only to hyperspectral images but also to other medical images such as MRI and CT images.…”

Section: Introductionmentioning

confidence: 99%

Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging

Halig

Wang

et al. 2014

J. Biomed. Opt

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Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors.

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Multi-Spectro-Temporal Analysis of Hyperspectral Imagery Based on 3-D Spectral Modeling and Multilinear Algebra

Cited by 17 publications

References 41 publications

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Tensor-based anomaly detection: An interdisciplinary survey

Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging

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