Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

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Trends in Deep Learning for Medical Hyperspectral Image Analysis

IEEE Access ◽

10.1109/access.2021.3068392 ◽

2021 ◽

pp. 1-1

Author(s):

Uzair Khan ◽

Sidike Paheding ◽

Colin Elkin ◽

Vijay Devabhaktuni

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Hyperspectral Image ◽

Hyperspectral Image Analysis

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Hyperspectral image analysis using deep learning — A review

2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) ◽

10.1109/ipta.2016.7820963 ◽

2016 ◽

Cited By ~ 17

Author(s):

Henrik Petersson ◽

David Gustafsson ◽

David Bergstrom

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Hyperspectral Image ◽

Hyperspectral Image Analysis

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Speech Enhancement Using Deep Learning Methods: A Review

Jurnal Elektronika dan Telekomunikasi ◽

10.14203/jet.v21.19-26 ◽

2021 ◽

Vol 21 (1) ◽

pp. 19

Author(s):

Asri Rizki Yuliani ◽

M. Faizal Amri ◽

Endang Suryawati ◽

Ade Ramdan ◽

Hilman Ferdinandus Pardede

Keyword(s):

Neural Network ◽

Deep Learning ◽

Speech Enhancement ◽

Speech Signal ◽

Research Field ◽

Learning Technologies ◽

Learning Approaches ◽

Speech Signal Processing ◽

Generative Adversarial Network ◽

Advantages And Disadvantages

Speech enhancement, which aims to recover the clean speech of the corrupted signal, plays an important role in the digital speech signal processing. According to the type of degradation and noise in the speech signal, approaches to speech enhancement vary. Thus, the research topic remains challenging in practice, specifically when dealing with highly non-stationary noise and reverberation. Recent advance of deep learning technologies has provided great support for the progress in speech enhancement research field. Deep learning has been known to outperform the statistical model used in the conventional speech enhancement. Hence, it deserves a dedicated survey. In this review, we described the advantages and disadvantages of recent deep learning approaches. We also discussed challenges and trends of this field. From the reviewed works, we concluded that the trend of the deep learning architecture has shifted from the standard deep neural network (DNN) to convolutional neural network (CNN), which can efficiently learn temporal information of speech signal, and generative adversarial network (GAN), that utilize two networks training.

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Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Sensors ◽

10.3390/s19183929 ◽

2019 ◽

Vol 19 (18) ◽

pp. 3929 ◽

Cited By ~ 22

Author(s):

Grigorios Tsagkatakis ◽

Anastasia Aidini ◽

Konstantina Fotiadou ◽

Michalis Giannopoulos ◽

Anastasia Pentari ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Large Body ◽

Super Resolution ◽

Imaging Systems ◽

Learning Approaches ◽

Language Understanding ◽

Learning Tasks ◽

Trade Offs ◽

Sensing Platforms

Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.

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Hyperspectral Image Classification Using Parallel Autoencoding Diabolo Networks on Multi-Core and Many-Core Architectures

Electronics ◽

10.3390/electronics7120411 ◽

2018 ◽

Vol 7 (12) ◽

pp. 411 ◽

Cited By ~ 4

Author(s):

Emanuele Torti ◽

Alessandro Fontanella ◽

Antonio Plaza ◽

Javier Plaza ◽

Francesco Leporati

Keyword(s):

Deep Learning ◽

Hyperspectral Imaging ◽

Hyperspectral Image ◽

Unsupervised Classification ◽

Hyperspectral Data ◽

Machine Learning Techniques ◽

High Data ◽

Learning Techniques ◽

Speed Up ◽

Many Core

One of the most important tasks in hyperspectral imaging is the classification of the pixels in the scene in order to produce thematic maps. This problem can be typically solved through machine learning techniques. In particular, deep learning algorithms have emerged in recent years as a suitable methodology to classify hyperspectral data. Moreover, the high dimensionality of hyperspectral data, together with the increasing availability of unlabeled samples, makes deep learning an appealing approach to process and interpret those data. However, the limited number of labeled samples often complicates the exploitation of supervised techniques. Indeed, in order to guarantee a suitable precision, a large number of labeled samples is normally required. This hurdle can be overcome by resorting to unsupervised classification algorithms. In particular, autoencoders can be used to analyze a hyperspectral image using only unlabeled data. However, the high data dimensionality leads to prohibitive training times. In this regard, it is important to realize that the operations involved in autoencoders training are intrinsically parallel. Therefore, in this paper we present an approach that exploits multi-core and many-core devices in order to achieve efficient autoencoders training in hyperspectral imaging applications. Specifically, in this paper, we present new OpenMP and CUDA frameworks for autoencoder training. The obtained results show that the CUDA framework provides a speed-up of about two orders of magnitudes as compared to an optimized serial processing chain.

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Near Infrared Hyperspectral Image Analysis Using R. Part 5: Animated Visualisation of Hyperspectral Data Using R and ImageJ

NIR news ◽

10.1255/nirn.1483 ◽

2014 ◽

Vol 25 (7) ◽

pp. 15-17 ◽

Cited By ~ 4

Author(s):

Y. Dixit ◽

R. Cama ◽

C. Sullivan ◽

L. Alvarez Jubete ◽

A. Ktenioudaki

Keyword(s):

Image Analysis ◽

Near Infrared ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Hyperspectral Image Analysis

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Deep learning for drug–drug interaction extraction from the literature: a review

Briefings in Bioinformatics ◽

10.1093/bib/bbz087 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1609-1627 ◽

Cited By ~ 5

Author(s):

Tianlin Zhang ◽

Jiaxu Leng ◽

Ying Liu

Keyword(s):

Deep Learning ◽

Drug Research ◽

Drug Effects ◽

Learning Technologies ◽

Biomedical Literature ◽

Learning Approaches ◽

Learning Methods ◽

Feature Representations ◽

Advantages And Disadvantages ◽

Interaction Extraction

AbstractDrug–drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been widely studied and emphasized in modern biomedical research. The previous rules-based and machine learning approaches rely on tedious feature engineering, which is labourious, time-consuming and unsatisfactory. With the development of deep learning technologies, this problem is alleviated by learning feature representations automatically. Here, we review the recent deep learning methods that have been applied to the extraction of DDIs from biomedical literature. We describe each method briefly and compare its performance in the DDI corpus systematically. Next, we summarize the advantages and disadvantages of these deep learning models for this task. Furthermore, we discuss some challenges and future perspectives of DDI extraction via deep learning methods. This review aims to serve as a useful guide for interested researchers to further advance bioinformatics algorithms for DDIs extraction from the literature.

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AN UNSUPERVISED LABELING APPROACH FOR HYPERSPECTRAL IMAGE CLASSIFICATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-407-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 407-415

Author(s):

J. González Santiago ◽

F. Schenkel ◽

W. Gross ◽

W. Middelmann

Keyword(s):

Hyperspectral Image ◽

Ground Truth ◽

Current Approach ◽

Hyperspectral Data ◽

Agglomerative Clustering ◽

Hyperspectral Image Analysis ◽

Hierarchical Agglomerative Clustering ◽

Benchmark Datasets ◽

Labeling Approach ◽

Class Labels

Abstract. The application of hyperspectral image analysis for land cover classification is mainly executed in presence of manually labeled data. The ground truth represents the distribution of the actual classes and it is mostly derived from field recorded information. Its manual generation is ineffective, tedious and very time-consuming. The continuously increasing amount of proprietary and publicly available datasets makes it imperative to reduce these related costs. In addition, adequately equipped computer systems are more capable of identifying patterns and neighbourhood relationships than a human operator. Based on these facts, an unsupervised labeling approach is presented to automatically generate labeled images used during the training of a convolutional neural network (CNN) classifier. The proposed method begins with the segmentation stage where an adapted version of the simple linear iterative clustering (SLIC) algorithm for dealing with hyperspectral data is used. Consequently, the Hierarchical Agglomerative Clustering (HAC) and Fuzzy C-Means (FCM) algorithms are employed to efficiently group similar superpixels considering distances with respect to each other. The distinct utilization of these clustering techniques defines a complementary stage for overcoming class overlapping during image generation. Ultimately, a CNN classifier is trained using the computed image to pixel-wise predict classes on unseen datasets. The labeling results, obtained using two hyperspectral benchmark datasets, indicate that the current approach is able to detect objects boundaries, automatically assign class labels to the entire dataset and to classify new data with a prediction certainty of 90%. Additionally, this method is also capable of achieving better classification accuracy and visual correspondence with reality than the ground truth images.

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A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Sensors ◽

10.3390/s22010263 ◽

2021 ◽

Vol 22 (1) ◽

pp. 263

Author(s):

Amal Altamimi ◽

Belgacem Ben Ben Youssef

Keyword(s):

Remote Sensing ◽

Systematic Review ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Sensor Technology ◽

Future Research ◽

Sensing Applications ◽

Remote Sensing Applications ◽

The Impact

Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

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