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Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment  ...  Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis.  ...  Dirichlet process mixture models have been used for semi-supervised land cover classification [161] , unmixing [221] , and endmember extraction [332, 333] .  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

Special Section Guest Editorial: Satellite Hyperspectral Remote Sensing: Algorithms and Applications

Kun Tan, Xiuping Jia, Antonio Plaza
2021 Journal of Applied Remote Sensing  
., where a mixture of spectral distance, spatial distance, and local outlier factor are used to assess the sample similarity.  ...  The unique spectral information of hyperspectral imagery can represent detailed features and provide a basis for ecological monitoring and assessment.  ...  These unique studies contributed to the development of satellite hyperspectral technology and laid the foundation for hyperspectral applications in a variety of fields.  ... 
doi:10.1117/1.jrs.15.042601 fatcat:ksl6q5deebdv3fmgmf2b2i6wym

The Role of Hyperspectral Imaging: A Literature Review

Muhammad Mateen, Junhao Wen, Nasrullah, Muhammad Azeem
2018 International Journal of Advanced Computer Science and Applications  
The proposed idea can be useful for further research in the field of hyperspectral imaging using deep learning.  ...  Optical analysis techniques are used recently to detect and identify the objects from a large scale of images. Hyperspectral imaging technique is also one of them.  ...  40] feature extraction [41] spatialspectral [39] Gaussian Mixture Models [42] un-supervised [43] [44] Latent Linear Models [45] feature extraction [46] dimensionality reduction [47] Ensemble  ... 
doi:10.14569/ijacsa.2018.090808 fatcat:54bc7yptrrddhcqd4snkbkuxna

The Novel Gravitational Mass Weighted PCA Technique for Feature Extraction in Hyperspectral Data Classification

2019 International Journal of Engineering and Advanced Technology  
Also, this paper presents the deep insight about the feature extraction techniques in hyperspectral data of both supervised and unsupervised learning methods and experimental analysis in AVIRIS Indian  ...  The above issues are overcome using feature extraction and feature selection methods which play a major role in the reduction of dimensionality.  ...  Over the decades, supervised and unsupervised techniques are widely used in the extraction of the features of HS data.  ... 
doi:10.35940/ijeat.e1056.0785s319 fatcat:w4fsxms6dbcmtkn4tbkfogzari

Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios [chapter]

Xiong Zhou, Saurabh Prasad
2020 Advances in Computer Vision and Pattern Recognition  
, semi-supervised and active learning approaches to image analysis, as well as transfer learning approaches for multi-source (e.g. multi-sensor, or multi-temporal) image analysis.  ...  These multi-channel images come with their own unique set of challenges that must be addressed for effective image analysis.  ...  Semi-supervised learning has been shown to be beneficial for hyperspectral image classification in various scenarios [48, 49, 50, 51, 52, 53] .  ... 
doi:10.1007/978-3-030-38617-7_5 fatcat:23ibk4ojbvepbpikxgjxan4i6e


V. H. Ayma, V. A. Ayma, J. Gutierrez
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
However, mining complementary features that reduce the redundancy from the multiple levels of hyperspectral images remains challenging.  ...  In this work, we propose a novel dimensionality reduction implementation for hyperspectral imaging based on autoencoders, ensuring the orthogonality among features to reduce the redundancy in hyperspectral  ...  In this work, we propose a novel semi-supervised approach to reduce the spectral dimensionality of hyperspectral images.  ... 
doi:10.5194/isprs-archives-xliii-b3-2020-357-2020 fatcat:hofoqz6n2ncunioej6eeawpxri

Hybrid Unmixing Based on Adaptive Region Segmentation for Hyperspectral Imagery

Xiangrong Zhang, Jingyan Zhang, Chen Li, Cai Cheng, Licheng Jiao, Huiyu Zhou
2018 IEEE Transactions on Geoscience and Remote Sensing  
Unmixing is an important issue of hyperspectral images. Most unmixing methods adopt linear mixing models for simplicity.  ...  Considering the characteristics of different regions in images, we propose a hybrid unmixing algorithm for hyperspectral images based on region adaptive segmentation (RASU).  ...  We extract the endmembers of the whole hyperspectral image firstly.  ... 
doi:10.1109/tgrs.2018.2815044 fatcat:2nhh2t5kdrettjikkrvklgbo7a

Hyperspectral Image Classification Based on Semi-Supervised Rotation Forest

2017 Remote Sensing  
Rotation forest (RoF), combining feature extraction and classifier ensembles, has been successfully applied to hyperspectral (HS) image classification by promoting the diversity of base classifiers since  ...  Therefore, in this paper, we present an improved ensemble learning method, which uses the semi-supervised feature extraction technique instead of PCA during the "rotation" process of classical RoF approach  ...  Weighted Semi-Supervised Local Discriminant Analysis Semi-supervised local discriminant analysis is a semi-supervised feature extraction method that has been applied in hyperspectral image classification  ... 
doi:10.3390/rs9090924 fatcat:ntzks7iuljcjfkubzdg3gvgh4m

Applications of Hyperspectral Remote Sensing in Ground Object Identification and Classification

Yu Wei, Xicun Zhu, Cheng Li, Xiaoyan Guo, Xinyang Yu, Chunyan Chang, Houxing Sun
2017 Advances in Remote Sensing  
The research results of identification and classification of forest tree species, grassland and urban land features were summarized. Then the researches of classification methods were summarized.  ...  On the basis of reviewing the application of hyperspectral remote sensing in identification and classification of ground objects at home and abroad.  ...  all kinds of features in the image are analyzed and selected.  ... 
doi:10.4236/ars.2017.63015 fatcat:hpd5nchv55b5zmopqukcffflua

Remote Sensing Satellite Images Classification Using Support Vector Machine and Particle Swarm Optimization

Omar S. Soliman, Amira S. Mahmoud, Safaa M. Hassan
2012 2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications  
With the help of semi supervised learning algorithm and multispectral sensing image the overall performance of PSNR is increased upto 42.98%.  ...  These compatibilities are handled with the help of segmentation and semi supervised learning algorithm.  ...  "Semiinput of a taken image is considered as the multi-purpose Supervised Classification Method For Hyperspectral classification.  ... 
doi:10.1109/ibica.2012.61 dblp:conf/ibica/SolimanMH12 fatcat:bey2ni63drbzpmnho3geryinue

Machine Learning for High-Throughput Stress Phenotyping in Plants

Arti Singh, Baskar Ganapathysubramanian, Asheesh Kumar Singh, Soumik Sarkar
2016 Trends in Plant Science  
However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping.  ...  Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants.  ...  Hicks for reviewing this article and Marcus Naik for helping with the development of figures.  ... 
doi:10.1016/j.tplants.2015.10.015 pmid:26651918 fatcat:bkrddy6mxjgrfozkym2qfful3m

Foreword to the special issue on hyperspectral remote sensing: Theory, methods, and applications

Qian Du, Liangpei Zhang, Bing Zhang, Xiaohua Tong, Peijun Du, Jocelyn Chanussot
2013 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Spectral Mixture Analysis Spectral mixture analysis (SMA) is very useful for hyperspectral image analysis.  ...  Thanks to the rapid advance in machine learning, the improvement of hyperspectral image classification is continued with the newly developed algorithms in semi-supervised learning, active learning, and  ... 
doi:10.1109/jstars.2013.2257422 fatcat:oaub3nkel5eqtnl7impnvmh5hu

Parallel implementation of linear and nonlinear spectral unmixing of remotely sensed hyperspectral images

Antonio Plaza, Javier Plaza, Bormin Huang, Antonio J. Plaza
2011 High-Performance Computing in Remote Sensing  
Hyperspectral unmixing is a very important task for remotely sensed hyperspectral data exploitation.  ...  Two models have been widely used in the literature in order to address the mixture problem in hyperspectral data.  ...  view of the imaging instrument. 4 The availability of hyperspectral imagers with a number of spectral bands that exceeds the number of spectral mixture components 5 has allowed to cast the unmixing  ... 
doi:10.1117/12.897326 fatcat:2omwkvitybguzkybeddo3szdei

Hyperspectral Image Classification [chapter]

Rajesh Gogineni, Ashvini Chaturvedi
2019 Processing and Analysis of Hyperspectral Data [Working Title]  
Hyperspectral image (HSI) classification is a phenomenal mechanism to analyze diversified land cover in remotely sensed hyperspectral images.  ...  Given a set of observations with known class labels, the basic goal of hyperspectral image classification is to assign a class label to each pixel.  ...  Based on the usage of training sample, image classification task is categorized as supervised, unsupervised and semi-supervised hyperspectral image classification.  ... 
doi:10.5772/intechopen.88925 fatcat:7ixv44bobbd3vkp7hn5c6tlb2y

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Analysis Technique for Near Real-Time In Situ Feature Extraction in Hyperspectral Imaging.  ...  ., +, JSTARS 2020 4325-4338 A New Parallel Dual-Channel Fully Convolutional Network Via Semi-Supervised FCM for PolSAR Image Classification.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
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