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Comparison and evaluation of quality criteria for hyperspectral imagery

Emmanuel Christophe, Dominique Leger, Corinne Mailhes, Rene Rasmussen, Yoichi Miyake
2005 Image Quality and System Performance II  
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

E. Christophe, D. Leger, C. Mailhes
2005 IEEE Transactions on Geoscience and Remote Sensing  
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

Ji Zhao, Suzheng Tian, Christian Geis, Lizhe Wang, Yanfei Zhong, Hannes Taubenbock
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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

Haoran Wu, Zhaoying Song, Xiaoyun Niu, Jun Liu, Jingmin Jiang, Yanjie Li
2022 Frontiers in Plant Science  
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

Hui Liu, Chenming Li, Lizhong Xu
2019 Computer Science and Information Systems  
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]

Jihan Khoder, Rafic Younes, Fethi Ben Ouezdou
2012 Lecture Notes in Computer Science  
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

Francesca Manni, Fons van der Sommen, Sveta Zinger, Esther Kho, Susan G. Brouwer de Koning, Theo J. M. Ruers, Caifeng Shan, Jean Schleipen, Peter H. N. de With, Cristian A. Linte, Baowei Fei
2019 Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling  
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

Yucel Cimtay, Hakki Gokhan Ilk
2017 Advances in Electrical and Electronic Engineering  
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

Purwadi, Nanna Suryana, Universitas Amikom Purwokerto, Purwokerto, Indonesia
2019 International Journal of Advanced Trends in Computer Science and Engineering  
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

Lianru Gao, Bin Zhao, Xiuping Jia, Wenzhi Liao, Bing Zhang
2017 Remote Sensing  
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

Jinling Zhao, Yan Fang, Guomin Chu, Hao Yan, Lei Hu, Linsheng Huang
2020 Plants  
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

S. Rinesh, K. Maheswari, B. Arthi, P. Sherubha, A. Vijay, S. Sridhar, T. Rajendran, Yosef Asrat Waji, Enas Abdulhay
2022 Journal of Healthcare Engineering  
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

Lianru Gao, Bing Zhang, Xu Sun, Shanshan Li, Qian Du, Changshan Wu
2013 EURASIP Journal on Advances in Signal Processing  
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

Imran Haider Khan, Haiyan Liu, Wei Li, Aizhong Cao, Xue Wang, Hongyan Liu, Tao Cheng, Yongchao Tian, Yan Zhu, Weixing Cao, Xia Yao
2021 Remote Sensing  
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

Yanfei Zhong, Tianyi Jia, Ji Zhao, Xinyu Wang, Shuying Jin
2017 Remote Sensing  
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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