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A COMPARISON STUDY OF DIFFERENT MARKER SELECTION METHODS FOR SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGES
2015
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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An effective approach based on the Minimum Spanning Forest (MSF), grown from automatically selected markers using Support Vector Machines (SVM), has been proposed for spectral-spatial classification of ...
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
Survey on Region Growing Segmentation and Classification for Hyperspectral Images
2013
International Journal of Computer Applications
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Image processing of hyperspectral image sector shows a thriving upbeat in innovation of new and novel techniques. ...
For obvious reasons, most of these apply to the process of image segmentation and classification, in which is the heart of image processing. ...
and do segmentation and classification processing based on the markers. ...
doi:10.5120/10144-4959
fatcat:ni7tdoeuofh6bhrup5r7rjyjue
Editorial for Special Issue "Hyperspectral Imaging and Applications"
2019
Remote Sensing
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The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore ...
Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. ...
of Hyperspectral Images Based on Extended Label Propagation and Rolling Guidance Filtering Binge Cui, Xiaoyun Xie, Siyuan Hao, Jiandi Cui and Yan Lu This paper proposes a semi-supervised classification ...
doi:10.3390/rs11172012
fatcat:c23u3rahgjhctowk5xwllt2qea
Combining multiscale features for classification of hyperspectral images: A sequence-based kernel approach
2016
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
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Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. ...
One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. ...
For comparison purposes, we also report the pixel-based classification overall accuracies. ...
doi:10.1109/whispers.2016.8071671
dblp:conf/whispers/CuiCL16
fatcat:ox6sddtj3rb2vdqm2l22covyj4
Combining classifiers for robust hyperspectral mapping using Hierarchical Trees and class memberships
2010
2010 IEEE International Geoscience and Remote Sensing Symposium
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processing chain (Maximum Likelihood after MNF rotation), 87.66% Fig. 2 allows also a visual comparison between the two corresponding maps while accuracy levels on a per class basis are provided in ...
The learning is based on the initial analysis of the available data and it optimizes the structure of a binary decision tree (BDTC) like ensemble in terms of nodes, inputs, and decision rules to be applied ...
doi:10.1109/igarss.2010.5649498
dblp:conf/igarss/BakosGZ10
fatcat:idpn3de3evgp7hiropmgwpo2wi
An Investigation of Image Segmentation Method for Remotely Sensed Hyperspectral Images with Region Object Aggregations
2016
International Journal of Computer Applications
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A paramount issue in image processing area is to design and implement an efficient segmentation and classification techniques demanding optimal resources. ...
This paper presents a survey on all prominent region growing segmentation techniques analyzing each one and thus sorting out an optimal and promising technique. ...
The Hierarchical image SEGmentation (HSEG) algorithm is a segmentation technique based on iterative hierarchical stepwise optimization region growing method. ...
doi:10.5120/ijca2016908379
fatcat:nzvzlo5kd5hujddmfaxk6jijj4
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach
[article]
2016
arXiv
pre-print
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Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. ...
One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. ...
For comparison purposes, we also report the pixel-based classification overall accuracies. ...
arXiv:1606.04985v1
fatcat:3l5teil3d5d5nfnorhgooctywq
Clustering-Based Hyperspectral Band Selection Using Information Measures
2007
IEEE Transactions on Geoscience and Remote Sensing
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Hyperspectral imaging involves large amounts of information. This paper presents a technique for dimensionality reduction to deal with hyperspectral images. ...
The proposed method is based on a hierarchical clustering structure to group bands to minimize the intracluster variance and maximize the intercluster variance. ...
Wang, both from RSSIPL, for their help in the implementation of the LCMV-CBS and CEM-CBS methods. ...
doi:10.1109/tgrs.2007.904951
fatcat:vrub4cwysjedrfl7mpuldqdzb4
Spatial-Spectral Random Patches Network for Classification of Hyperspectral Images
2019
Traitement du signal
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The integrated feature vectors inherit the merits of the deep hierarchical features of both RPNet and G-RPNet, laying a solid basis for the classification of hyperspectral images. ...
The results prove the superiority of the proposed method in the classification of hyperspectral images over some recent shallow and deep spatial-spectral classification techniques. ...
The analyses of results are summarized as follows: • Based on the results of Table 1 , the classification of hyperspectral images with the only spectral band cannot lead to good results due to the spectral ...
doi:10.18280/ts.360504
fatcat:ucd44ntflrdefo3cf3i4dfhlpy
Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network
2020
Sensors
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The experimental results showed that the overall classification accuracy was based on the proposed hierarchical-fusion multiscale dilated residual network (M-DRN), which reached an accuracy of 97.89%. ...
Finally, a hierarchical fusion scheme was applied to the hyperspectral CASI and airborne LiDAR features, and the fused features were used to train a residual network for high-accuracy ground object classification ...
Conflicts of Interest: The authors declare no conflict of interest. Sensors 2020, 20, 3961 ...
doi:10.3390/s20143961
pmid:32708693
pmcid:PMC7412085
fatcat:6owydygp5vag3evdj7plczl3te
Unsupervised Clustering for Hyperspectral Images
2020
Symmetry
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This paper presents a comparison of unsupervised hyperspectral image classification such as K-means, Hierarchical clustering, and Parafac decomposition, which allows the performance of the model reduction ...
Hyperspectral images are becoming a valuable tool much used in agriculture, mineralogy, and so on. ...
All authors have read and agreed to the published version of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/sym12020277
fatcat:hkadq5jak5emjpsjzu43p5rwki
Deep Learning-Based Man-Made Object Detection from Hyperspectral Data
[chapter]
2015
Lecture Notes in Computer Science
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In this paper, we tackle the problem of man-made object detection from hyperspectral data through a deep learning classification framework. ...
By the effective exploitation of a Convolutional Neural Network we encode pixels' spectral and spatial information and employ a Multi-Layer Perceptron to conduct the classification task. ...
We tackle the problem of man-made object detection through a deep learning classification framework using on hyperspectral data. ...
doi:10.1007/978-3-319-27857-5_64
fatcat:vkkj5e5gg5d2jcdhjhlzrqnidi
Lossy-To-Lossless Block-Based Compression of Hyperspectral Volumetric Data
2006
2006 International Conference on Image Processing
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An embedded, block-based, wavelet transform coding algorithm of low complexity is proposed. ...
Three-Dimensional Set Partitioned Embedded bloCK(3D-SPECK) efficiently encodes hyperspectral volumetric image data by exploiting the dependencies in all dimensions. ...
Motta [4] et al. proposed a VQ based algorithm that involved locally optimal design of a partitioned vector quantizer for the encoding of source vectors drawn from hyperspectral images. ...
doi:10.1109/icip.2006.312756
dblp:conf/icip/TangP06
fatcat:mfpmetsgjbhn5k2xmouokk4caq
RECURSIVE HIERARCHICAL CLUSTERING FOR HYPERSPECTRAL IMAGES
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The recursive hierarchical approach reduces the algorithm complexity, in order to process large amount of input pixels, and also to produce a clustering with a high number of clusters. ...
We propose in this paper to use a recursive hierarchical clustering based on standard clustering strategies such as K-Means or Fuzzy-C-Means. ...
INTRODUCTION
The problem of labeling Hyperspectral images give us access to a wide range on information contained in the different spectral bands. ...
doi:10.5194/isprs-archives-xliii-b3-2020-461-2020
fatcat:cvphdinvubgwjlhb2js6q3i56a
SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING A HYBRID FRAMEWORK
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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There are many algorithms for classification. This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. ...
Classification is a key issue in processing hyperspectral images. Spectral-identification-based algorithms are sensitive to spectral variability and noise in acquisition. ...
(Québec, Canada) for providing the hyperspectral dataset used in this research. ...
doi:10.5194/isprs-archives-xlii-4-w18-41-2019
fatcat:omgfqny3nvcrvn4ydqemqonh7u
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