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Comparison and evaluation of quality criteria for hyperspectral imagery
2005
Image Quality and System Performance II
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This paper proposes a method to evaluate quality criteria. The purpose is to provide quality criteria corresponding well to the impact of degradation on end-user applications. ...
Handling the significant size of hyperspectral data presents a challenge for the user community. ...
While SAM classification is not sensitive to the presence of white noise in the image, Mahalanobis classification and Maximum Likelihood classification are very sensitive to additive white noise. ...
doi:10.1117/12.587107
fatcat:isnup4bzkjaitixuciofkisex4
Quality criteria benchmark for hyperspectral imagery
2005
IEEE Transactions on Geoscience and Remote Sensing
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This paper proposes a method to evaluate quality criteria in the context of hyperspectral images. ...
The purpose is to provide quality criteria relevant to the impact of degradations on several classification applications. Different quality criteria are considered. ...
ACKNOWLEDGMENT The authors wish to thank the National Aeronautics and Space Administration Jet Propulsion Laboratory for providing the hyperspectral images used during the experiments. ...
doi:10.1109/tgrs.2005.853931
fatcat:pxbcaaoh6zb2zfuplz6n2bomvm
Spectral-spatial classification integrating band selection for hyperspectral imagery with severe noise bands
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The CRFBS algorithm can achieve accurate interpretation of the various classification categories and a more than 3% improvement in classification accuracy, compared with the method using the original hyperspectral ...
The experiments using different airborne and UAV-borne hyperspectral data verified the effectiveness of the CRFBS method. ...
Ji Zhao gratefully acknowledges the support of K. C. Wong Education Foundation and German Academic Exchange Service. ...
doi:10.1109/jstars.2020.2984568
fatcat:mszx2i4qmzdufmyqnl2jthhkxa
Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery
2022
Frontiers in Plant Science
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In this study, UAV hyperspectral imaging technology was used to obtain canopy hyperspectral data of biennial seedlings of different varieties of T. sinensis to distinguish young and old leaves. ...
In this study, a fast and effective method for identifying young leaves of T. sinensis was found, which provided a reference for the rapid identification of young leaves of T. sinensis in the wild. ...
In this study, the classification of young and old leaves of T. sinensis using UAV hyperspectral imaging technology combined with different preprocessing methods and machine learning algorithms was studied ...
doi:10.3389/fpls.2022.940327
pmid:35837456
pmcid:PMC9274089
fatcat:7ilmx66ijjhnhjkoipv7xecb6u
Dimension reduction and classification of hyperspectral images based on neural network sensitivity analysis and multi-instance learning
2019
Computer Science and Information Systems
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In our proposed method, we combined neural network sensitivity analysis with a multiinstance learning algorithm based on a support vector machine to achieve dimension reduction and accurate classification ...
Experimental results demonstrated that our method provided strong overall classification effectiveness when compared with prior methods. . ...
Finally, we compared the classification performance of the proposed method with some other hyperspectral image classification methods, as shown in Table 9 . ...
doi:10.2298/csis180428003l
fatcat:gpakb2jwwvesdgfxvasoor2eoq
Stability of Dimensionality Reduction Methods Applied on Artificial Hyperspectral Images
[chapter]
2012
Lecture Notes in Computer Science
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This paper introduces a study stability of the non parametric and unsupervised methods of projection and of bands selection used in dimensionality reduction of different noise levels determined with different ...
The performances of the method are verified on artificial data sets for validation. ...
We can conclude from this study on the robustness of the hybrid method BandClust / MDS, which found very encouraging results on the stability of hyperspectral data compared to the influence of noise during ...
doi:10.1007/978-3-642-33564-8_56
fatcat:bt6fyg2danbezhhwyfbkv6x3qu
Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging
2019
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
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The method follows a machinelearning based classification, employing linear support vector machine (SVM), and offers a superior sensitivity and a significant decrease in computation time. ...
The HSI combined with the proposed classification reaches a sensitivity of 94%, specificity of 68% and area under the curve (AUC) of 92%. ...
ACKNOWLEDGMENTS The research activity leading to the results of this paper was funded by the H2020-ECSEL Joint Undertaking under Grant Agreement N o 692470 (ASTONISH Project). ...
doi:10.1117/12.2512238
dblp:conf/miigp/ManniSZKKRSSW19
fatcat:nsm7lqplnnh4nnnjsnfpscj3zq
A Novel Derivative-Based Classification Method for Hyperspectral Data Processing
2017
Advances in Electrical and Electronic Engineering
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The method is tested for hyperspectral images which are captured at different time of the year and different places, in the life cycle of species. ...
As a solution to time dependency and spectral similarity problems, in this study, a new and more generic method based on the spectral derivative is proposed. ...
Acknowledgment We would like to thank HAVELSAN for letting us use the hyperspectral data in this study. ...
doi:10.15598/aeee.v15i4.2381
fatcat:jyg6cotqcfghtboo26tgvtbsda
A Comprehensive Review: Classification Techniques on Hyperspectral Remote Sensing
2019
International Journal of Advanced Trends in Computer Science and Engineering
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Based on these problems, this study aims to review various classification methods used to solve existing classification problems. ...
The results of this study recommend classification techniques that can be used based on performance parameters. ...
ACKNOWLEDGEMENT The authors would like to thank the financial support from Department of Informatics, Faculty of Computer Science, Universitas Amikom Purwokerto and Pusat Teknologi Pengkomputeran Termaju ...
doi:10.30534/ijatcse/2019/3181.52019
fatcat:3kbgewtygra75amchskvcnwopq
Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification
2017
Remote Sensing
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PCA and MNF are two of the widely adopted methods for dimensionality reduction of hyperspectral images. As we all know, the performance of PCA highly relies on noise characteristics [26, 28] . ...
While MNF is a valuable dimensionality reduction method for hyperspectral image classification, it is found that the traditional version of MNF cannot provide desired results in real applications. ...
Here, we conduct a comparative study for noise estimation algorithms using real images with different land cover types. ...
doi:10.3390/rs9060548
fatcat:pektfgx3vjcufkdzg4wgaa6ify
Identification of Leaf-Scale Wheat Powdery Mildew (Blumeria graminis f. sp. Tritici) Combining Hyperspectral Imaging and an SVM Classifier
2020
Plants
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In this study, an integral method was proposed based on a hyperspectral imaging dataset and machine learning algorithms. ...
The results show that the SVM model constructed by PCA dimensionality reduction had the best result, and the classification accuracy reached 93.33% by a cross-validation method. ...
Conflicts of Interest: The authors declare no conflicts of interest. ...
doi:10.3390/plants9080936
pmid:32722022
fatcat:dqjpuwg7sjhfngp4rsuwa7vflm
Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms
2022
Journal of Healthcare Engineering
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This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. ...
The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute ...
Based on the image classification accuracy, the mean absolute error value, and the peak signal-to-noise ratio, the results are compared with existing image classification methods and neural network models ...
doi:10.1155/2022/2761847
pmid:35198132
pmcid:PMC8860516
fatcat:htfgl5vxgrfsrniynvlizwnc5i
Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data
2013
EURASIP Journal on Advances in Signal Processing
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First, a new noise estimation method, named residual-scaled local standard deviations, is used to analyze the noise condition of HJ-1A hyperspectral images. ...
The proposed OMNF method is less sensitive to noise distribution and interference existence, thus it can more efficiently compact useful data information in a low-dimensional space. ...
This drawback limits its application for hyperspectral images, which generally have very different types of noise. ...
doi:10.1186/1687-6180-2013-65
fatcat:m2fsjsgnfjaa3lspf3uq7tibxq
Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat
2021
Remote Sensing
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Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. ...
This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs13183612
fatcat:6gndjtaeevemfhn6ep4bxahntm
Spatial-Spectral-Emissivity Land-Cover Classification Fusing Visible and Thermal Infrared Hyperspectral Imagery
2017
Remote Sensing
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In this paper, in order to make full use of these two types of imagery, a spatial-spectral-emissivity land-cover classification method based on the fusion of visible and thermal infrared hyperspectral ...
radiance from thermal infrared hyperspectral imagery data, with a kappa value of 0.9137. ...
Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2017, 9, 910 ...
doi:10.3390/rs9090910
fatcat:ft26adflizblzfcxhsoyazz2pe
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