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Survey on Region Growing Segmentation and Classification for Hyperspectral Images

S. ArokiaJeromeGeorge, S. John Livingston
2013 International Journal of Computer Applications  
For obvious reasons, most of these apply to the process of image segmentation and classification, in which is the heart of image processing.  ...  Image processing of hyperspectral image sector shows a thriving upbeat in innovation of new and novel techniques.  ...  SEGMENTATION AND CLASSIFICATION ON HYPERSPECTRAL IMAGES There are plenty of researches has been done and plenty of method's are available in the field of segmentation for classifying hyperspectral image  ... 
doi:10.5120/10144-4959 fatcat:ni7tdoeuofh6bhrup5r7rjyjue

An Investigation of Image Segmentation Method for Remotely Sensed Hyperspectral Images with Region Object Aggregations

A. Nirmala
2016 International Journal of Computer Applications  
A paramount issue in image processing area is to design and implement an efficient segmentation and classification techniques demanding optimal resources.  ...  The reported results give clear evidence of the relevance of using both spatial and spectral information in hyper spectral image segmentation.  ...  profiles were also considered to integrate spatial information in the classification of hyperspectral images [13] .  ... 
doi:10.5120/ijca2016908379 fatcat:nzvzlo5kd5hujddmfaxk6jijj4

Best merge region growing with integrated probabilistic classification for hyperspectral imagery

Yuliya Tarabalka, James C. Tilton
2011 2011 IEEE International Geoscience and Remote Sensing Symposium  
A new method for spectral-spatial classification of hyperspectral images is proposed.  ...  Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana's vegetation area and compared with those obtained by recently proposed spectral-spatial classification techniques  ...  For this purpose, a new Hierarchical Segmentation with integrated Classification (HSwC) method for hyperspectral data is proposed.  ... 
doi:10.1109/igarss.2011.6050034 dblp:conf/igarss/TarabalkaT11 fatcat:q4zz7w6ubzelhijlkcafnqxzoe

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  
Hyperspectral image classification method consists of supervised classification, unsupervised classification and semi-supervised classification.  ...  The results of this study recommend classification techniques that can be used based on performance parameters.  ...  (C-ACT), Fakulti Teknologi Maklumat dan Komunikasi (FTMK), Universiti Teknikal Malaysia Melaka (UTeM) for their assistance in this research  ... 
doi:10.30534/ijatcse/2019/3181.52019 fatcat:3kbgewtygra75amchskvcnwopq

A COMPARISON STUDY OF DIFFERENT MARKER SELECTION METHODS FOR SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGES

D. Akbari, A. R. Safari, S. Homayouni, S. Khazai
2015 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Moreover, the pixels of each class, with the largest population in the classification map, are kept for each region of the segmentation map.  ...  hyperspectral images by Tarabalka et al.  ...  Landgrebe and Larry Biehl from Purdue University, U.S. and the German Aerospace Centre (DLR) for Berlin hyperspectral dataset.  ... 
doi:10.5194/isprsarchives-xl-1-w5-37-2015 fatcat:4qtcm2lnyvfabdcmajfbk7lvze

Advanced Processing of Hyperspectral Images

A. Plaza, J.A. Benediktsson, J.W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P.E. Gamba, A. Gaultieri, J.C. Tilton
2006 2006 IEEE International Symposium on Geoscience and Remote Sensing  
In this paper, we provide a seminal view on recent advances in techniques for hyperspectral data processing.  ...  Hyperspectral imaging offers the possibility of characterizing materials and objects in the air, land and water on the basis of the unique reflectance patterns that result from the interaction of solar  ...  CLASSIFICATION AND SEGMENTATION TECHNIQUES A.  ... 
doi:10.1109/igarss.2006.511 dblp:conf/igarss/PlazaBBBBCCFGGTT06 fatcat:mq2bfupfx5cyxbzyjeqnybpwli

Maize Seed Variety Classification Using the Integration of Spectral and Image Features Combined with Feature Transformation Based on Hyperspectral Imaging

Min Huang, Chujie He, Qibing Zhu, Jianwei Qin
2016 Applied Sciences  
Hyperspectral imaging (HSI) technology has been extensively studied in the classification of seed variety.  ...  Subsequently, spectral and imaging features were extracted from regions of interest of the hyperspectral images.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app6060183 fatcat:suidnd6oejdpzkcvkqhpiuybxi

Segmentation and classification of hyperspectral images using watershed transformation

Y. Tarabalka, J. Chanussot, J.A. Benediktsson
2010 Pattern Recognition  
The integration of the spatial information from the watershed segmentation in the hyperspectral image classifier improves the classification accuracies and provides classification maps with more homogeneous  ...  Experimental segmentation and classification results are presented on two hyperspectral images.  ...  The authors would like to thank Paolo Gamba and David Landgrebe for providing the hyperspectral data and James Tilton for providing the NASA Goddard's RHSEG software.  ... 
doi:10.1016/j.patcog.2010.01.016 fatcat:rifqfbvcubg3li6nrnxyh5okbm

Combining Hyperspectral and Lidar Data for Vegetation Mapping in the Florida Everglades

Caiyun Zhang
2014 Photogrammetric Engineering and Remote Sensing  
A framework was designed to integrate two remotely sensed datasets and four data processing techniques.  ...  The study shows the promise of the synergy of hyperspectral and lidar systems for mapping complex wetlands.  ...  Zhixiao Xie assisted with the image segmentation. Dr. Prasad S. Thenkabail provided valuable comments and suggestions, which improved an earlier version of this paper.  ... 
doi:10.14358/pers.80.8.733 fatcat:7zdbtd6qrfawxlodzwlocdvdca

Marker-based Hierarchical Segmentation and classification approach for hyperspectral imagery

Yuliya Tarabalka, James C. Tilton, Jon Atli Benediktsson, Jocelyn Chanussot
2011 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary.  ...  In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, a map of markers is constructed using classification results.  ...  Unlike most other segmentation techniques, the HSEG generates at its output a hierarchical set of image segmentations.  ... 
doi:10.1109/icassp.2011.5946597 dblp:conf/icassp/TarabalkaTBC11 fatcat:vfqej5nasfdvfiw3s6nirfopey

AN EXTENDED SPECTRAL–SPATIAL CLASSIFICATION APPROACH FOR HYPERSPECTRAL DATA

D. Akbari
2017 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed.  ...  In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm.  ...  Gamba from the University of Pavia, Italy, for providing the hyperspectral data.  ... 
doi:10.5194/isprs-annals-iv-4-w4-37-2017 fatcat:5mwgll3twjcd7afbqec5ma7kn4

A New Methodology for Spectral-Spatial Classification of Hyperspectral Images

Zelang Miao, Wenzhong Shi
2016 Journal of Sensors  
The segmentation map is subsequently integrated with the pixel-wise classification method to classify the hyperspectral image.  ...  Recent developments in hyperspectral images have heightened the need for advanced classification methods.  ...  Acknowledgments The work presented in this paper is partly supported by Ministry of Science and Technology of China (Project nos.  ... 
doi:10.1155/2016/1538973 fatcat:6tgh2g4ktffkxkaynqpayky54i

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Tatireddy Reddy, Jonnadula Harikiran
2022 Journal of Spectral Imaging  
Moreover, this paper shows the effectiveness of all these techniques for hyperspectral image classification and dimensionality reduction.  ...  Hyperspectral imaging is used in a wide range of applications.  ...  To tackle these issues, Jiang et al. 83 segmented the hyperspectral image into superpixels of several scales.  ... 
doi:10.1255/jsi.2022.a1 fatcat:rue5klkmlfcrzftepc6lzfcbfe

Object-Based Land Cover Classification Using Airborne Lidar and Different Spectral Images

Tee-Ann Teo, Chun-Hsuan Huang
2016 Terrestrial, Atmospheric and Oceanic Sciences  
Object-based classification was implemented using image segmentation and supervised classification.  ...  Therefore, the multisensor data can be integrated to obtain useful information for different applications. This study integrates lidar with different spectral features for land cover classification.  ...  Acknowledgements The authors would like to thank the Ministry of Interior of Taiwan (project number SYC1030219) for the providing test data and funding support.  ... 
doi:10.3319/tao.2016.01.29.01(isrs) fatcat:o4zgsiodl5hplo4nwdww2zsqmu

A Review on Advances in Automated Plant Disease Detection

Radhika Bhagwat, Yogesh Dandawate
2021 International Journal of Engineering and Technology Innovation  
in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved.  ...  The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection.  ...  [50] proposed an integrated method using hyperspectral imaging that included segmenting the plant, spectrum classification, and classification of image for early recognition of tomato spotted wilt virus  ... 
doi:10.46604/ijeti.2021.8244 fatcat:ebucqz5c55ewjlsdquayjqoyv4
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