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Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering
2021
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
unpublished
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In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly. ...
Then, a multiple graph diffusion strategy with theoretical convergence guarantee is designed to learn a unified graph for partitioning the whole hyperspectral cube into several subcubes via spectral clustering ...
Conclusion In this paper, we present a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering (RMGF). ...
doi:10.24963/ijcai.2021/418
fatcat:t7dz35ii6zhfhb2j7a4x5gtjhu
An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques
2022
Journal of Spectral Imaging
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As a result, this paper reviews three different types of hyperspectral image machine learning classification methods: cluster analysis, supervised and semi-supervised classification. ...
Hyperspectral imaging is used in a wide range of applications. ...
For hyperspectral images, an Expectation-Maximisation (EM) algorithm-based clustering with band fusion is offered by Prabukumar and Shrutika, 76 and a Band Correlation Clustering (BCC)-based feature ...
doi:10.1255/jsi.2022.a1
fatcat:rue5klkmlfcrzftepc6lzfcbfe
Machine learning based hyperspectral image analysis: A survey
[article]
2019
arXiv
pre-print
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This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature. ...
regression, support vector machines, Gaussian mixture model, latent linear models, sparse linear models, Gaussian mixture models, ensemble learning, directed graphical models, undirected graphical models, clustering ...
It has been used primarily for band selection by clustering the bands in an image and selecting a representative band for each cluster [157, 326] . ...
arXiv:1802.08701v2
fatcat:bfi6qkpx2bf6bowhyloj2duugu
Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs
2018
Remote Sensing
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In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). ...
The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. ...
IFRF combines spatial and spectral information via image fusion and recursive filtering. ...
doi:10.3390/rs10081271
fatcat:onawzc6cyjdyjmxdl4kg32wnoa
A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES
2017
International Journal on Smart Sensing and Intelligent Systems
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The spectral and spatial information reflected from the original Hyperspectral Images with four various features. ...
A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. ...
Rajakumar A review on multiple-feature-based adaptive sparse representation (MFASR) and other classification types Figure. 2 . 2 (a) Pixel-wise multiple-feature-based joint sparse model (b) Pixel-wise ...
doi:10.21307/ijssis-2017-224
fatcat:k2x24hgfkjctxh3jwjssq5esle
Regularized Sparse Band Selection via Learned Pairwise Agreement
2020
IEEE Access
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Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularized Band Selection via Learned ...
In RBS-LPA, a spatial-spectral informative graph, constructed by spatial-spectral neighbor relationship, is incorporated to encode both the spatial and spectral geometrical structure. ...
bands are reselected to preserve the graph structure. (4) By incorporating the geometrical information of the data space via constructing a neighbor graph regularizer, we ensure that the spatial spectral ...
doi:10.1109/access.2020.2971556
fatcat:72u7qlvq75cpnatvv773xlpxcq
Blind Hyperspectral-Multispectral Image Fusion via Graph Laplacian Regularization
[article]
2019
arXiv
pre-print
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Fusing a low-resolution hyperspectral image (HSI) and a high-resolution multispectral image (MSI) of the same scene leads to a super-resolution image (SRI), which is information rich spatially and spectrally ...
Experiments on various datasets demonstrate the advantages of the proposed algorithm in the quality of fusion and its capability in dealing with unknown spatial degradation. ...
The spectral degradation from the SRI to the MSI can be modeled by a weighted summation of the hyperspectral bands according to the spectral responses of the multispectral sensor. ...
arXiv:1902.08224v1
fatcat:csmycrol5bg3pnvft4tgov6dyq
Diverse-Region Hyperspectral Image Classification via Superpixelwise Graph Convolution Technique
2022
Remote Sensing
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Specifically, the proposed method is operated on non-Euclidean graphs, which are constructed by superpixel segmentation methods for diverse regions to cluster irregularly local-region pixels. ...
hyperspectral data correctly. ...
In these years, multiple superpixel-based methods were proposed to extract the spectral-spatial features via segmentation methods [41] [42] [43] [44] . ...
doi:10.3390/rs14122907
fatcat:2oeomww76vh2zhuzt4mhxvtuju
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art
2017
IEEE Geoscience and Remote Sensing Magazine
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Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information, which make a plethora of applications for the ...
This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality ...
Technical Committee for organizing the 2013 Data Fusion Contest. ...
doi:10.1109/mgrs.2017.2762087
fatcat:6ezzye7yyvacbouduqv2f2c7gi
Semisupervised Hyperspectral Band Selection Via Spectral–Spatial Hypergraph Model
2015
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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We show that hypergraph can captures relationship among pixels in both spectral and spatial domain. ...
Band selection is an essential step towards effective and efficient hyperspectral image classification. ...
This is due to the better measurement of similarities between hyperspectral pixels via capturing of both spectral and spatial relationship between multiple samples.
G. ...
doi:10.1109/jstars.2015.2443047
fatcat:wxtmoeoke5hobavexp7wdyyns4
A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Recent developments in hyperspectral sensors have made it possible to acquire HyperSpectral Images (HSI) with higher spectral and spatial resolution. ...
Because of this, superpixels have been successfully applied to various fields of HSI processing like Classification, Spectral Unmixing, Dimensionality Reduction, Band Selection, Active Learning, Denoising ...
classification via multiple kernels (SCMK). ...
doi:10.1109/jstars.2021.3076005
fatcat:smfb6jeox5eldbv6ys7ioeoko4
Spectral-Spatial Hyperspectral Imagery Classification Using Robust Dual-Stage Spatial Embedding
2021
IEEE Access
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ERS is a graph-based clustering method that generates superpixels by performing graph partitioning. ...
The image has a spatial dimension of 145 × 145 and 220 spectral bands with a spatial resolution of 20 m per pixel (20 water absorption bands were removed before experiments). ...
doi:10.1109/access.2021.3099631
fatcat:zus2rvnfcvgjnns4chdae7k6wy
2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57
2019
IEEE Transactions on Geoscience and Remote Sensing
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., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674 Hu, C., Zhang, ...
Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging on Arbitrary Region ...
., +, TGRS May 2019 2741-2753 Multiple-Feature Kernel-Based Probabilistic Clustering for Unsupervised Band Selection. ...
doi:10.1109/tgrs.2020.2967201
fatcat:kpfxoidv5bgcfo36zfsnxe4aj4
Hyperspectral Image Classification with Localized Graph Convolutional Filtering
2021
Remote Sensing
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In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. ...
Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. ...
a graph-based model considering both spatial and spectral information [46] . ...
doi:10.3390/rs13030526
fatcat:y7bp3lgrazdtlclamfdqpgdhfe
Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification
2021
Remote Sensing
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Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features. ...
First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification ...
(c) RVCANET, (d), hyperspectral images based on random multi-graphs (HC-RMG), (e), spectral-spatial hyperspectral classification via structural-kernel collaborative representation (SKCR) (f) HS_AGU, and ...
doi:10.3390/rs13040547
fatcat:dlyfs4fxj5bcpknnoxku4z6rjy
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