Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

Signoroni, Alberto; Savardi, Mattia; Baronio, Annalisa; Benini, Sergio

doi:10.3390/jimaging5050052

Cited by 264 publications

(146 citation statements)

References 217 publications

(284 reference statements)

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“…In future research, we intend to integrate microwave remote sensing and higher resolution data to reduce the impact of cloud cover, snow cover, and melt ponds on sea ice detection. In addition, because of the great potential of deep learning in automatic feature extraction and learning model building, it is widely used in various fields [41]. However, it requires large datasets and long training time.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning

et al. 2019

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Sea ice is one of the causes of marine disasters. The classification of sea ice images is an important part of sea ice detection. The labeled samples in hyperspectral sea ice image classification are difficult to acquire, which causes minor sample problems. In addition, most of the current sea ice classification methods mainly use spectral features for shallow learning, which also limits further improvement of the sea ice classification accuracy. Therefore, this paper proposes a hyperspectral sea ice image classification method based on the spectral-spatial-joint feature with deep learning. The proposed method first extracts sea ice texture information by the gray-level co-occurrence matrix (GLCM). Then, it performs dimensionality reduction and a correlation analysis of the spectral information and spatial information of the unlabeled samples, respectively. It eliminates redundant information by extracting the spectral-spatial information of the neighboring unlabeled samples of the labeled sample and integrating the information with the spectral and texture data of the labeled sample to further enhance the quality of the labeled sample. Lastly, the three-dimensional convolutional neural network (3D-CNN) model is designed to extract the deep spectral-spatial features of sea ice. The proposed method combines relevant textural features and performs spectral-spatial feature extraction based on the 3D-CNN model by using a large amount of unlabeled sample information. In order to verify the effectiveness of the proposed method, sea ice classification experiments are carried out on two hyperspectral data sets: Baffin Bay and Bohai Bay. Compared with the CNN algorithm based on a single feature (spectral or spatial) and other CNN algorithms based on spectral-spatial features, the experimental results show that the proposed method achieves better sea ice classification (98.52% and 97.91%) with small samples. Therefore, it is more suitable for classifying hyperspectral sea ice images.

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Section: Discussionmentioning

confidence: 99%

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning

et al. 2019

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“…Deep learning has recently shown remarkable results in many computer vision application [66], [67]. A recent review [68] reported several applications of deep learning for hyperspectral imagery. In the paper [69], a convolution neural network (CNN) has been proposed for the sparse band selection of hyperspectral face recognition.…”

Section: Deep Learning and Hyperspectral Imagingmentioning

confidence: 99%

Current Advances in Hyperspectral Face Recognition

Qureshi¹,

Uzair²,

Zahra³

2020

Preprint

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Hyperspectral imaging systems are well established, for satellite, remote sensing and geosciences applications. Recently, the reduction in the cost of hyperspectral sensors and increase in the imaging speed has attracted computer vision scientists to apply hyperspectral imaging to ground based computer vision problems such as material classification, agriculture, chemistry and document image analysis. Hyperspectral imaging has also been explored for face recognition; to tackle the issues of pose and illumination variations by exploiting the richer spectral information of hyperspectral images. In this article, we present a detailed review on the potential of hyperspectral imaging for face recognition. We present hyperspectral image aquisition process and discuss key preprocessing challenges. We also discuss hyperspectral face recognition databases and techniques for feature extraction from the hyperspectral images. Potential future research directions are also highlighted

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“…In recent years, deep learning, a more advanced machine learning algorithm capable of learning complex relationships, extracting feature patterns and directly building predictive models from big data, has been applied in numerous fields . Many studies have reported excellent deep learning model performances in the chemistry and biology fields. In this study, we applied a self‐designed convolutional neural network framework named DeepIR to the pulmonary edema fluid spectra involving five causes of death to assess its performance in determining the cause of death.…”

Section: Introductionmentioning

confidence: 99%

Determination of causes of death via spectrochemical analysis of forensic autopsies‐based pulmonary edema fluid samples with deep learning algorithm

Lin

Luo

Sun

et al. 2020

Journal of Biophotonics

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This study investigated whether infrared spectroscopy combined with a deep learning algorithm could be a useful tool for determining causes of death by analyzing pulmonary edema fluid from forensic autopsies. A newly designed convolutional neural network-based deep learning framework, named DeepIR and eight popular machine learning algorithms, were used to construct classifiers. The prediction performances of these classifiers demonstrated that DeepIR outperformed the machine learning algorithms in establishing classifiers to determine the causes of death. Moreover, DeepIR wasgenerally less dependent on preprocessing procedures than were the machine learning algorithms; it provided the validation accuracy with a narrow range from 0.9661 to 0.9856 and the test accuracy ranging from 0.8774 to 0.9167 on the raw pulmonary edema fluid spectral dataset and the nine preprocessing protocolbased datasets in our study. In conclusion, this study demonstrates that the deep learning-equipped Fourier transform infrared spectroscopy technique has the potential to be an effective aid for determining causes of death. K E Y W O R D S chemometrics, deep learning, forensic science, infrared spectroscopy, pulmonary edema fluid

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Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

Cited by 264 publications

References 217 publications

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning

Current Advances in Hyperspectral Face Recognition

Determination of causes of death via spectrochemical analysis of forensic autopsies‐based pulmonary edema fluid samples with deep learning algorithm

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