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Journal articles on the topic 'Hyperspectral data analysis'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Landgrebe, D. "Hyperspectral image data analysis." IEEE Signal Processing Magazine 19, no. 1 (2002): 17–28. http://dx.doi.org/10.1109/79.974718.

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Tsai, Fuan, and William Philpot. "Derivative Analysis of Hyperspectral Data." Remote Sensing of Environment 66, no. 1 (1998): 41–51. http://dx.doi.org/10.1016/s0034-4257(98)00032-7.

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3

Zaman, Zainab, Saad Bin Ahmed, and Muhammad Imran Malik. "Analysis of Hyperspectral Data to Develop an Approach for Document Images." Sensors 23, no. 15 (2023): 6845. http://dx.doi.org/10.3390/s23156845.

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Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications. In this manuscript, we have compiled and presented a comprehensive overview of recent advances in hyperspectral data analysis that can provide assistance for the development of customized techniques for hyperspectral document images. We review the fundamental concepts of hyperspectral imaging, discuss various techniques for data acquisition, and examine state-of-the-art approaches to the preprocessing, featu

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Qi, Beixun. "Hyperspectral Image Database Query Based on Big Data Analysis Technology." E3S Web of Conferences 275 (2021): 03018. http://dx.doi.org/10.1051/e3sconf/202127503018.

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In this paper, we extract spectral image features from a hyperspectral image database, and use big data technology to classify spectra hierarchically, to achieve the purpose of efficient database matching. In this paper, the LDMGI (local discriminant models and global integration) algorithm and big data branch definition algorithm are used to classify the features of the hyperspectral image and save the extracted feature data. Hierarchical color similarity is used to match the spectrum. By clustering colors, spectral information can be stored as chain nodes in the database, which can improve t

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Pervez, W., S. A. Khan, and Valiuddin. "HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 169–75. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-169-2015.

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Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is

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., Y. Sai Praveen. "IMAGE FUSION ANALYSIS FOR HYPERSPECTRAL DATA." International Journal of Research in Engineering and Technology 05, no. 19 (2016): 18–21. http://dx.doi.org/10.15623/ijret.2016.0519004.

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Burger, James, and Aoife Gowen. "Data handling in hyperspectral image analysis." Chemometrics and Intelligent Laboratory Systems 108, no. 1 (2011): 13–22. http://dx.doi.org/10.1016/j.chemolab.2011.04.001.

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Wu, Zebin, Jinping Gu, Yonglong Li, Fu Xiao, Jin Sun, and Zhihui Wei. "Distributed Parallel Endmember Extraction of Hyperspectral Data Based on Spark." Scientific Programming 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3252148.

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Due to the increasing dimensionality and volume of remotely sensed hyperspectral data, the development of acceleration techniques for massive hyperspectral image analysis approaches is a very important challenge. Cloud computing offers many possibilities of distributed processing of hyperspectral datasets. This paper proposes a novel distributed parallel endmember extraction method based on iterative error analysis that utilizes cloud computing principles to efficiently process massive hyperspectral data. The proposed method takes advantage of technologies including MapReduce programming model

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Liu, Jing, Tingting Wang, and Yulong Qiao. "Depth and Width Changeable Network-Based Deep Kernel Learning-Based Hyperspectral Sensor Data Analysis." Wireless Communications and Mobile Computing 2021 (February 20, 2021): 1–8. http://dx.doi.org/10.1155/2021/8842396.

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Sensor data analysis is used in many application areas, for example, Artificial Intelligence of Things (AIoT), with the rapid developing of the deep neural network learning that promotes its application area. In this work, we propose the Depth and Width Changeable Deep Kernel Learning-based hyperspectral sensing data analysis algorithm. Compared with the traditional kernel learning-based hyperspectral data classification, the proposed method has its advantages on the hyperspectral data classification. With the deep kernel learning, the feature is mapped through many times mapping and has the m

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Zhu, Wei Hong, and Cheng Zhe Xu. "Hyperspectral Data Analysis for Detecting Lead Pollution in Rice." Applied Mechanics and Materials 433-435 (October 2013): 456–59. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.456.

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This paper presents a new method for detecting lead pollution in rice by analyzing hyperspectral data. First, preprocessing method is used to remove the outliers which deviate so much from other hyperspectral data. Then, dimensionality-reduced data are made by using discrete wavelet transform. Finally, linear discriminant analysis is utilized to extract the feature which characterizes polluted and unpolluted rice. The experimental result based on the proposed method shows the good performance in detecting lead pollution in rice.

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Thenkabail, Prasad S. "Hyperspectral Data Processing: Algorithm Design and Analysis." Photogrammetric Engineering & Remote Sensing 81, no. 6 (2015): 441–42. http://dx.doi.org/10.14358/pers.81.6.441.

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12

Hong Li, Guangrun Xiao, Tian Xia, Y. Y. Tang, and Luoqing Li. "Hyperspectral Image Classification Using Functional Data Analysis." IEEE Transactions on Cybernetics 44, no. 9 (2014): 1544–55. http://dx.doi.org/10.1109/tcyb.2013.2289331.

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Filik, Jacob, Abigail V. Rutter, Josep Sulé-Suso, and Gianfelice Cinque. "Morphological analysis of vibrational hyperspectral imaging data." Analyst 137, no. 24 (2012): 5723. http://dx.doi.org/10.1039/c2an35914f.

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14

Sugianto, S., M. Rusdi, and Syakur. "Functional Data Analysis: An Initiative Approach for Hyperspectral Data." Journal of Physics: Conference Series 1363 (November 2019): 012087. http://dx.doi.org/10.1088/1742-6596/1363/1/012087.

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15

Duponchel, Ludovic. "Exploring hyperspectral imaging data sets with topological data analysis." Analytica Chimica Acta 1000 (February 2018): 123–31. http://dx.doi.org/10.1016/j.aca.2017.11.029.

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Dong, Shao, and Yi Lin. "Data quality analysis after hyperspectral LiDAR sequentially mapping trees." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1-2024 (May 9, 2024): 49–57. http://dx.doi.org/10.5194/isprs-annals-x-1-2024-49-2024.

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Abstract. Light detection and ranging (LiDAR), as an innovative remote sensing tool, not only captures target reflectance but also provides its morphological parameters. Traditional single/multi-band LiDAR and multispectral LiDAR (MSL) are presently employed in applications such as 3D modeling and plant biochemical parameter inversion albeit with effectiveness limited. Moreover, hyperspectral LiDAR (HSL) distinguished by its expanded array of spectral detection channels and enhanced spectral resolution, has proven more effective in meeting these requirements and also exhibits superior capabili

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Sugianto, Sugianto, and Shawn Laffan. "Functional Data Analysis of Multi-Angular Hyperspectral Data on Vegetation." Aceh International Journal of Science and Technology 1, no. 1 (2012): 30–39. http://dx.doi.org/10.13170/aijst.1.1.12.

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Abstract - The surface reflectance anisotropy can be estimated by directional reflectance analysis through the collection of multi-angular spectral data. Proper characterization of the surface anisotropy is an important element in the successful interpretation of remotely sensed signals. A signal received by a sensor from a vegetation canopy is affected by several factors. One of them is the sensor zenith angle. Functional data analysis can be used to assess the distribution and variation of spectral reflectance due to sensor zenith angle. This paper examines the effect of sensor zenith angles

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Aoyanagi, Yoshihide, Tomofumi Doi, Hajime Arai, et al. "On-Orbit Performance and Hyperspectral Data Processing of the TIRSAT CubeSat Mission." Remote Sensing 17, no. 11 (2025): 1903. https://doi.org/10.3390/rs17111903.

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A miniaturized hyperspectral camera, developed by integrating a linear variable band-pass filter (LVBPF) with an image sensor, was installed on the TIRSAT 3U CubeSat, launched on 17 February 2024 by Japan’s H3 launch vehicle. The satellite and its onboard hyperspectral camera conducted on-orbit experiments and successfully acquired hyperspectral data from multiple locations. The required attitude control for the hyperspectral mission was also achieved. CubeSat-based hyperspectral missions often face challenges in image alignment due to factors such as parallax, distortion, and limited attitude

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Praveen, Bishwas, and Vineetha Menon. "A Bidirectional Deep-Learning-Based Spectral Attention Mechanism for Hyperspectral Data Classification." Remote Sensing 14, no. 1 (2022): 217. http://dx.doi.org/10.3390/rs14010217.

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Hyperspectral remote sensing presents a unique big data research paradigm through its rich information captured across hundreds of spectral bands, which embodies vital spatial and temporal information about the underlying land cover. Deep-learning-based hyperspectral data analysis methodologies have made significant advancements over the past few years. Despite their success, most deep learning frameworks for hyperspectral data classification tend to suffer in terms of computational and classification efficacy as the data size increases. This is largely due to their equal emphasis criteria on

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Sakarya, U. "Hyperspectral dimension reduction using global and local information based linear discriminant analysis." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-7 (September 19, 2014): 61–66. http://dx.doi.org/10.5194/isprsannals-ii-7-61-2014.

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Hyperspectral image classification has become an important research topic in remote sensing. Because of high dimensional data, a special attention is needed dealing with spectral data; and thus, one of the research topics in hyperspectral image classification is dimension reduction. In this paper, a dimension reduction approach is presented for classification on hyperspectral images. Advantages of the usage of not only global pattern information, but also local pattern information are examined in hyperspectral image processing. In addition, not only tuning the parameters, but also an experimen

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Ayuba, Daniel La’ah, Jean-Yves Guillemaut, Belen Marti-Cardona, and Oscar Mendez. "HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis." Remote Sensing 16, no. 18 (2024): 3399. http://dx.doi.org/10.3390/rs16183399.

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The use of a pretrained image classification model (trained on cats and dogs, for example) as a perceptual loss function for hyperspectral super-resolution and pansharpening tasks is surprisingly effective. However, RGB-based networks do not take full advantage of the spectral information in hyperspectral data. This inspired the creation of HyperKon, a dedicated hyperspectral Convolutional Neural Network backbone built with self-supervised contrastive representation learning. HyperKon uniquely leverages the high spectral continuity, range, and resolution of hyperspectral data through a spectra

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Di Benedetto, Alessia, Luìs Manuel de Almieda Nieto, Alessia Candeo, Gianluca Valentini, Daniela Comelli, and Matthias Alfeld. "Multivariate analysis on fused hyperspectral datasets within Cultural Heritage field." EPJ Web of Conferences 309 (2024): 14007. http://dx.doi.org/10.1051/epjconf/202430914007.

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This work introduces a novel method to multivariate analysis applied to fused hyperspectral datasets in the field of Cultural Heritage (CH). Hyperspectral Imaging is a well-established approach for the non-invasive examination of artworks, offering insights into their composition and conservation status. In CH field, a combination of hyperspectral techniques is usually employed to reach a comprehensive understanding of the artwork. To deal with hyperspectral data, multivariate statistical methods are essential due to the complexity of the data. The process involves factorizing the data matrix

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23

Pizzolante, Raffaele. "Editorial Paper for the Special Issue “Algorithms in Hyperspectral Data Analysis”." Algorithms 15, no. 4 (2022): 112. http://dx.doi.org/10.3390/a15040112.

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24

Lim, Alane. "Autonomous data collection allows focus on analysis rather than acquisition." Scilight 2023, no. 13 (2023): 131101. http://dx.doi.org/10.1063/10.0017783.

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Paringer, R. A., A. V. Mukhin, and A. V. Kupriyanov. "Formation of an informative index for recognizing specified objects in hyperspectral data." Computer Optics 45, no. 6 (2021): 873–78. http://dx.doi.org/10.18287/2412-6179-co-930.

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The paper is about the development of an approach which able to create rules for distinguish-ing between specified objects of hyperspectral data using a small number of observations. Such an approach would contribute to the development of methods and algorithms for the operational analysis of hyperspectral data. These methods can be used for hyperspectral data preprocessing and labeling. Implementation of the proposed approach are using a technology that harnesses both discriminative criteria and the general formulas of spectral indexes. In implementing the proposed technology, the index was d

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26

Wang, S., and C. Wang. "Research on dimension reduction method for hyperspectral remote sensing image based on global mixture coordination factor analysis." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (June 26, 2015): 159–67. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-159-2015.

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Over the past thirty years, the hyperspectral remote sensing technology is attracted more and more attentions by the researchers. The dimension reduction technology for hyperspectral remote sensing image data is one of the hotspots in current research of hyperspectral remote sensing. In order to solve the problems of nonlinearity, the high dimensions and the redundancy of the bands that exist in the hyperspectral data, this paper proposes a dimension reduction method for hyperspectral remote sensing image data based on the global mixture coordination factor analysis. In the first place, a line

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27

Brown, A. J. "Spectral curve fitting for automatic hyperspectral data analysis." IEEE Transactions on Geoscience and Remote Sensing 44, no. 6 (2006): 1601–8. http://dx.doi.org/10.1109/tgrs.2006.870435.

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28

Borhani, Mostafa, and Hassan Ghassemian. "Kernel Multivariate Spectral–Spatial Analysis of Hyperspectral Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 6 (2015): 2418–26. http://dx.doi.org/10.1109/jstars.2015.2399936.

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Chen, Yushi, Shunli Ma, Xi Chen, and Pedram Ghamisi. "Hyperspectral data clustering based on density analysis ensemble." Remote Sensing Letters 8, no. 2 (2016): 194–203. http://dx.doi.org/10.1080/2150704x.2016.1249295.

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30

Bioucas-Dias, Jose M., Antonio Plaza, Gustavo Camps-Valls, Paul Scheunders, Nasser Nasrabadi, and Jocelyn Chanussot. "Hyperspectral Remote Sensing Data Analysis and Future Challenges." IEEE Geoscience and Remote Sensing Magazine 1, no. 2 (2013): 6–36. http://dx.doi.org/10.1109/mgrs.2013.2244672.

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Zhifeng, Zhang, Cui Xiao, Pu Li, Jiang Jintao, and Ji Xiaohui. "Hyperspectral Data Analysis based on Integrated Deep Learning." International Journal of Performability Engineering 16, no. 8 (2020): 1225. http://dx.doi.org/10.23940/ijpe.20.08.p9.12251234.

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Makantasis, Konstantinos, Anastasios D. Doulamis, Nikolaos D. Doulamis, and Antonis Nikitakis. "Tensor-Based Classification Models for Hyperspectral Data Analysis." IEEE Transactions on Geoscience and Remote Sensing 56, no. 12 (2018): 6884–98. http://dx.doi.org/10.1109/tgrs.2018.2845450.

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33

Zhao, Mingyang, Min Huang, Lulu Qian, Zhanchao Wang, Wenhao Zhao, and Zixuan Zhang. "Design and Implementation of Airborne Hyperspectral Camera Control and Data Storage System." Journal of Physics: Conference Series 2617, no. 1 (2023): 012015. http://dx.doi.org/10.1088/1742-6596/2617/1/012015.

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Abstract With the great progress of hyperspectral imaging technology, airborne hyperspectral cameras have been widely used in the field of earth detection and remote sensing. However, with the increasing resolution of airborne hyperspectral cameras, the amount of data acquired is also growing explosive, The speed and accuracy in the transmission process are easily affected, which poses challenges to the real-time control and data acquisition and storage of airborne hyperspectral cameras. In this paper, a general airborne hyperspectral image acquisition and storage system is designed, which con

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Ismail, Mohamed, and Milica Orlandić. "Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures." Algorithms 13, no. 12 (2020): 330. http://dx.doi.org/10.3390/a13120330.

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Hyperspectral image classification has been increasingly used in the field of remote sensing. In this study, a new clustering framework for large-scale hyperspectral image (HSI) classification is proposed. The proposed four-step classification scheme explores how to effectively use the global spectral information and local spatial structure of hyperspectral data for HSI classification. Initially, multidimensional Watershed is used for pre-segmentation. Region-based hierarchical hyperspectral image segmentation is based on the construction of Binary partition trees (BPT). Each segmented region

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Lim, Hwan-Hui, Seung-Rae Lee, Enok Cheon, and Yeeun Nam. "Soil Water Content Regression Analysis of Measurement Data from Hyperspectral Camera in Weathered Granite Soils." E3S Web of Conferences 415 (2023): 03017. http://dx.doi.org/10.1051/e3sconf/202341503017.

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Soil water content is one of the most common physical parameters that cause landslides or debris flow. Therefore, it is of very importance to determine or predict the water content quickly and non-destructively. This study investigates the hyperspectral information in the visible near-infrared regions (VNIR) of different samples of granite soils possessing varying water content. Totally 162 granite samples were taken from each mountain area. The samples with different water contents were examined using a hyperspectral radiometer operating in the 400~1000nm range by obtaining the spectral curve

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Wander, Lukas, Alvise Vianello, Jes Vollertsen, Frank Westad, Ulrike Braun, and Andrea Paul. "Exploratory analysis of hyperspectral FTIR data obtained from environmental microplastics samples." Analytical Methods 12, no. 6 (2020): 781–91. http://dx.doi.org/10.1039/c9ay02483b.

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37

Borana, S. L., S. K. Yadav, and R. T. Paturkar. "DISCRIMINATION AND CHARACTERIZATION OF PROMINENT DESERTIC VEGETATIONS USING HYPERSPECTRAL IMAGING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 363–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-363-2019.

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<p><strong>Abstract.</strong> Imaging Hyperspectral data are advent as potential solutions in modeling, discrimination and mapping of vegetation species. Hyperspectral remote sensing provides valuable information about vegetation type, leaf area index, chlorophyll, and leaf nutrient concentration. Estimation of these vegetation parameters has been made possible by calculating various vegetation indices (VIs), usually by ratioing, differencing, ratioing differences and combinations of suitable spectral band. This paper presents a ground-based hyperspectral imaging system for c

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Pei, Zhongming, Yong Mao Huang, and Ting Zhou. "Review on Analysis Methods Enabled by Hyperspectral Imaging for Cultural Relic Conservation." Photonics 10, no. 10 (2023): 1104. http://dx.doi.org/10.3390/photonics10101104.

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In this review, the conservation methods for various types of cultural relics enabled by hyperspectral imaging are summarized, and the hyperspectral cameras and techniques utilized in the process from data acquisition to analyzation are introduced. Hyperspectral imaging is characterized by non-contact detection, broadband, and high resolution, which are of great significance to the non-destructive investigation of cultural relics. However, owing to the wide variety of cultural relics, the utilized equipment and methods vary greatly in the investigations of the associated conservation. Previous

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Priyadarshini, K. Nivedita, V. Sivashankari, Sulochana Shekhar, and K. Balasubramani. "Comparison and Evaluation of Dimensionality Reduction Techniques for Hyperspectral Data Analysis." Proceedings 24, no. 1 (2019): 6. http://dx.doi.org/10.3390/iecg2019-06209.

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Hyperspectral datasets provide explicit ground covers with hundreds of bands. Filtering contiguous hyperspectral datasets potentially discriminates surface features. Therefore, in this study, a number of spectral bands are minimized without losing original information through a process known as dimensionality reduction (DR). Redundant bands portray the fact that neighboring bands are highly correlated, sharing similar information. The benefits of utilizing dimensionality reduction include the ability to slacken the complexity of data during processing and transform original data to remove the

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Machidon, Alina L., Fabio Del Frate, Matteo Picchiani, Octavian M. Machidon, and Petre L. Ogrutan. "Geometrical Approximated Principal Component Analysis for Hyperspectral Image Analysis." Remote Sensing 12, no. 11 (2020): 1698. http://dx.doi.org/10.3390/rs12111698.

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Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent hyperspectral image processing algorithms. This paper introduces the PCA approximation method based on a geometric construction approach (gaPCA) method, an alternative algorithm for computing the principal components based on a geometrical constructed approximation of the standard PCA and presents its application to remote sensing hyperspectral images.

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Marwaha, R., A. Kumar, P. L. N. Raju, and Y. V. N. Krishna Murthy. "Target detection algorithm for airborne thermal hyperspectral data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 827–32. http://dx.doi.org/10.5194/isprsarchives-xl-8-827-2014.

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Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm<sup>&minus;1</sup> to 1280 cm<sup

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Akbari, D. "AN EXTENDED SPECTRAL–SPATIAL CLASSIFICATION APPROACH FOR HYPERSPECTRAL DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 37–41. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-37-2017.

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In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE),

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Kuzmina, Margarita Georgievna. "Multilayered autoencoders in problems of hyperspectral image analysis and processing." Keldysh Institute Preprints, no. 28 (2021): 1–21. http://dx.doi.org/10.20948/prepr-2021-28.

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A model of five-layered autoencoder (stacked autoencoder, SAE) is suggested for deep image features extraction and deriving compressed hyperspectral data set specifying the image. Spectral cost function, dependent on spectral curve forms of hyperspectral image, has been used for the autoencoder tuning. At the first step the autoencoder capabilities will be tested based on using pure spectral information contained in image data. The images from well known and widely used hyperspectral databases (Indian Pines, Pavia University и KSC) are planned to be used for the model testing.

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Jahan, F., and M. Awrangjeb. "PIXEL-BASED LAND COVER CLASSIFICATION BY FUSING HYPERSPECTRAL AND LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 711–18. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-711-2017.

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Land cover classification has many applications like forest management, urban planning, land use change identification and environment change analysis. The passive sensing of hyperspectral systems can be effective in describing the phenomenology of the observed area over hundreds of (narrow) spectral bands. On the other hand, the active sensing of LiDAR (Light Detection and Ranging) systems can be exploited for characterising topographical information of the area. As a result, the joint use of hyperspectral and LiDAR data provides a source of complementary information, which can greatly assist

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Suran, N. A., H. Z. M. Shafri, N. S. N. Shaharum, N. A. W. M. Radzali, and V. Kumar. "UAV-BASED HYPERSPECTRAL DATA ANALYSIS FOR URBAN AREA MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 621–26. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-621-2019.

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Abstract. A recent development in low-cost technology such as Unmanned Aerial Vehicle (UAV) offers an easy method for collecting geospatial data. UAV plays an important role in land resource surveying, urban planning, environmental protection, pollution monitoring, disaster monitoring and other applications. It is a highly adaptable technology that is continuously changing in innovative ways to provide greater utility. Thus, this study aimed to evaluate the capability of UAV-based hyperspectral data for urban area mapping. In order to do the mapping, Artificial Neural Network (ANN), Support Ve

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Lu, Yinghui, and Fuqing Zhang. "Toward Ensemble Assimilation of Hyperspectral Satellite Observations with Data Compression and Dimension Reduction Using Principal Component Analysis." Monthly Weather Review 147, no. 10 (2019): 3505–18. http://dx.doi.org/10.1175/mwr-d-18-0454.1.

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Abstract Satellite-based hyperspectral radiometers usually have thousands of infrared channels that contain atmospheric state information with higher vertical resolution compared to observations from traditional sensors. However, the large numbers of channels can lead to computational burden in satellite data retrieval and assimilation. Furthermore, most of the channels are highly correlated and the pieces of independent information contained in the hyperspectral observations are usually much smaller than the number of channels. Principal component analysis (PCA) was used in this research to c

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Minkin, A. S., O. V. Nikolaeva, and A. A. Russkov. "Hyperspectral data compression based upon the principal component analysis." Computer Optics 45, no. 2 (2021): 235–44. http://dx.doi.org/10.18287/2412-6179-co-806.

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The paper is aimed at developing an algorithm of hyperspectral data compression that combines small losses with high compression rate. The algorithm relies on a principal component analysis and a method of exhaustion. The principal components are singular vectors of an initial signal matrix, which are found by the method of exhaustion. A retrieved signal matrix is formed in parallel. The process continues until a required retrieval error is attained. The algorithm is described in detail and input and output parameters are specified. Testing is performed using AVIRIS data (Airborne Visible-Infr

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Brook, Anna, and Eyal Ben-Dor. "Advantages of the Boresight Effect in Hyperspectral Data Analysis." Remote Sensing 3, no. 3 (2011): 484–502. http://dx.doi.org/10.3390/rs3030484.

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Oskouie, Majid Mohammady, and Wolfgang Busch. "A Geostatistically Based Preprocessing Algorithm for Hyperspectral Data Analysis." GIScience & Remote Sensing 45, no. 3 (2008): 356–68. http://dx.doi.org/10.2747/1548-1603.45.3.356.

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Kumar, Chandan, Snehamoy Chatterjee, Thomas Oommen, and Arindam Guha. "New effective spectral matching measures for hyperspectral data analysis." International Journal of Remote Sensing 42, no. 11 (2021): 4126–56. http://dx.doi.org/10.1080/01431161.2021.1890265.

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