Integration of hyperspectral imaging and autoencoders: Benefits, applications, hyperparameter tunning and challenges

被引：45

作者：

Jaiswal, Garima ^{[1
]}

Rani, Ritu ^{[2
]}

Mangotra, Harshita ^{[2
]}

Sharma, Arun ^{[2
]}

机构：

[1] Bennett Univ, Greater Noida, India

[2] Indira Gandhi Delhi Tech Univ Women, Delhi, India

来源：

COMPUTER SCIENCE REVIEW | 2023年 / 50卷

关键词：

Hyperspectral imaging; Autoencoders; Classification; Hyperspectral unmixing; Anomaly detection; STACKED AUTOENCODER; FEATURE-EXTRACTION; CLASSIFICATION; NETWORK; WAVELET; IMAGES;

D O I：

10.1016/j.cosrev.2023.100584

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Hyperspectral imaging (HSI) is a powerful tool that can capture and analyze a range of spectral bands, providing unparalleled levels of precision and accuracy in data analysis. Another technology gaining popularity in many industries is Autoencoders (AE). AE uses advanced deep learning algorithms for encoding and decoding data, leading to highly precise and efficient neural network-based models. Within the domain of HSI, AE emerges as a potent approach to tackle the essential hurdles associated with data analysis and feature extraction. Combining both HSI and AE (HSI - AE) can lead to a revolution in various industries, including but not limited to healthcare and environmental monitoring, because of more efficient analysis approaches and decision-making. AE can be used to discover hidden patterns and insights in large-scale datasets, allowing researchers to make more informed decisions based on much better predictions. Similarly, HSI can benefit from the scalability and flexibility AE offers, leading to faster and more efficient data processing. This article aims to provide a comprehensive review of the integration of HSI -AE, covering the history and background knowledge, motivation, and combined benefits of HSI and AE. It examines the applicability of HSI-AE in many use-case domains, such as classification, hyperspectral unmixing, and anomaly detection. It also provides a hyperparameter tuning and an in-depth survey of their use. The article emphasizes crucial areas for future exploration, such as conducting further research to enhance AE's performance in HSI applications and devising novel algorithms to overcome the distinctive challenges presented by HSI data. Overall, the culmination of the HSI with AE can be seen as offering a promising solution for challenges like data analysis management and pattern recognition, enabling accurate and efficient decision-making across industries.& COPY; 2023 Elsevier Inc. All rights reserved.

引用

页数：19

共 108 条

[1] Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry [J].

Adao, Telmo ;

Hruska, Jonas ;

Padua, Luis ;

Bessa, Jose ;

Peres, Emanuel ;

Morais, Raul ;

Sousa, Joaquim Joao .

REMOTE SENSING, 2017, 9 (11)

[2] Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification [J].

Ahmad, Muhammad ;

Shabbir, Sidrah ;

Oliva, Diego ;

Mazzara, Manuel ;

Distefano, Salvatore .

OPTIK, 2020, 206

[3] Multi-layer Extreme Learning Machine-based Autoencoder for Hyperspectral Image Classification [J].

Ahmad, Muhammad ;

Khan, Adil Mehmood ;

Mazzara, Manuel ;

Distefano, Salvatore .

VISAPP: PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 4, 2019, :75-82

[4] Deep Learning for Classification of Hyperspectral Data [J].