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On Spectral-Spatial Classification of Hyperspectral Images Using Image Denoising and Enhancement Techniques, Wavelet Transforms and Controlled Data Set Partitioning
2022
Remote Sensing
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We propose a hyperspectral image classification machine learning framework based on image processing techniques for denoising and enhancement and a parallel approach for the feature extraction step. ...
Obtaining relevant classification results for hyperspectral images depends on the quality of the data and the proposed selection of the samples and descriptors for the training and testing phases. ...
Classification accuracy for the Indian Pines dataset, without filtering, with wavelet transform up to three levels, with the corresponding window size, of dimension 3 × 3 and 5 × 5, for the controlled ...
doi:10.3390/rs14061475
fatcat:lsw2l5t4ujbx7fzdr7bzfj4yoi
Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing
2023
Computer systems science and engineering
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In the second step, this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information. ...
The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range. The proposed scheme is well- ...
Spectral Dimensionality Reduction A band averaging approach minimizes the hyperspectral dimensions. It reduces the computational cost of extraction function and image denoising of the original HSI. ...
doi:10.32604/csse.2023.034374
fatcat:wmwva5xktbgcdmhkxlexo4g6n4
Classification of hyperspectral images by tensor modeling and additive morphological decomposition
2013
Pattern Recognition
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Pixel-wise classification in high-dimensional multivariate images is investigated. The proposed method deals with the joint use of spectral and spatial information provided in hyperspectral images. ...
Experimental comparison shows that the proposed algorithm can provide better performance for pixel classification of hyperspectral image than many other well-known techniques. ...
On the one hand, dimension reduction of multivariate images is one of the main subject of interest for the hyperspectral community. ...
doi:10.1016/j.patcog.2012.08.011
fatcat:4jlvbypvq5dijemxszby5pec3q
On the Sampling Strategy for Evaluation of Spectral-Spatial Methods in Hyperspectral Image Classification
2017
IEEE Transactions on Geoscience and Remote Sensing
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Spectral-spatial processing has been increasingly explored in remote sensing hyperspectral image classification. ...
To partially solve this problem, we propose a novel controlled random sampling strategy for spectral-spatial methods. ...
However, for hyperspectral images, the random sampling is usually undertaken on the same image. ...
doi:10.1109/tgrs.2016.2616489
fatcat:qg6riailyfhj5ntdshhvfsah7e
A Two-Staged Feature Extraction Method Based on Total Variation for Hyperspectral Images
2022
Remote Sensing
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In the second stage, equipped with singular value transformation to reduce the dimension again, we followed an isotropic TV model based on split Bregman algorithm for further detail smoothing. ...
Effective feature extraction (FE) has always been the focus of hyperspectral images (HSIs). ...
[11] , and maximum noise fraction (MNF) [12] , which are dedicated to linearly transforming the data into a low-dimensional feature space and which reduce the band dimension of HSIs. ...
doi:10.3390/rs14020302
fatcat:ugztwh4al5hgllrwhiphqmbwci
Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images
2015
IEEE Transactions on Geoscience and Remote Sensing
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First, the spectral dimension of the hyperspectral image is reduced with averaging-based image fusion. Then, the dimension reduced image is partitioned into several subsets of adjacent bands. ...
In this paper, a novel feature extraction method based on intrinsic image decomposition (IID) is proposed for hyperspectral image classification. The proposed method consists of the following steps. ...
ACKNOWLEDGMENT The authors would like to thank the Editor-in-Chief, the anonymous Associate Editor, and the reviewers for their insightful comments and suggestions, which have greatly improved the paper ...
doi:10.1109/tgrs.2014.2358615
fatcat:b2oolnadtncgld44iujihul6ma
Structure Extraction with Total Variation for Hyperspectral Image Classification
2019
IEEE Access
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INDEX TERMS Structure extraction, hyperspectral image classification, total variation, fusion. ...
This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation ...
Hyperspectral image (HSI) can be acquired by these hyperspectral sensors. ...
doi:10.1109/access.2019.2922675
fatcat:vmjzazc7wrac7pemey56am6owy
Ensemble EMD-based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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reduction for HSI is performed by using principal component analysis method. ...
The experimental results of three authentic hyperspectral data sets show that the proposed algorithm obtains superior classification performance compared with other methods. ...
Furthermore, a large number of non-linear HSI dimension reduction methods have also been proposed for HIC [20] . ...
doi:10.1109/jstars.2020.3018710
fatcat:jlr6mk2npnffzkwwk4xovkcrje
A Metrological Measurement of Texture in Hyperspectral Images Using Relocated Spectral Difference Occurrence Matrix
2019
2019 IEEE International Conference on Image Processing (ICIP)
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The performance is close to Opponent Band Local Binary Pattern (OBLBP) with classification accuracy of 94.7%, but at a much-reduced feature size (0.24% of OBLBP's) and computational complexity. ...
For metrological purposes, rSDOM employs Kullback-Leibler pseudo-divergence (KLPD) for spectral difference calculation. It is generic and adapted for any spectral range and number of band. ...
Hyperspectral image processing is often faced with the curse of dimensionality due to the large number of spectral bands. ...
doi:10.1109/icip.2019.8803378
dblp:conf/icip/ChuRFH19
fatcat:m4dgqrw6m5c3vdtktchjckemyy
2015 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 53
2015
IEEE Transactions on Geoscience and Remote Sensing
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., +, TGRS Feb. 2015 920-932
Dimension Reduction Using Spatial and Spectral Regularized Local Dis-
criminant Embedding for Hyperspectral Image Classification. ...
Wu, Y., +, TGRS Jan. 2015 440-452 Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral Image Classification. ...
doi:10.1109/tgrs.2015.2513444
fatcat:zuklkpk4gjdxjegoym5oagotzq
A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
2023
Smart Agricultural Technology
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2023 pre-print arXiv:2306.09418v1 fatcat:rbcwlleo7naf7g5kcme6mrpioi
Dealing with the curse of dimensionality presents another challenge due to the abundance of spectral and spatial information in each hyperspectral cube. ...
Due to its wide spectral range, compared with multispectral- or RGB-based imaging methods, HSI can be a more effective tool for monitoring crop health and productivity. ...
Dimensionality reduction techniques The image shows the rendered hyperspectral image after black and white image correction using the Equation 2 . retain relevant information, while allowing the model ...
doi:10.1016/j.atech.2023.100316
fatcat:qoyqnfiocfcmlpxlnrlb24x57q
Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation
2015
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Due to the low-dimensional property of clean hyperspectral images (HSIs), many low-rank-based methods have been proposed to denoise HSIs. ...
Index Terms-Denoising, hyperspectral image (HSI), low-rank matrix approximation (LRMA), noise-adjusted iteration, randomized singular value decomposition (RSVD). ...
Landgrebe at Purdue University for providing the free downloads of the HYDICE image of the Washington DC Mall, and Prof. D. Tao and T. Zhou for the GoDec algorithm code. ...
doi:10.1109/jstars.2015.2398433
fatcat:5xumq4235nfzfnsqxfyt552ixa
Empirical Mode Decomposition of Hyperspectral Images for Support Vector Machine Classification
2010
IEEE Transactions on Geoscience and Remote Sensing
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In the first approach, IMFs corresponding to each hyperspectral image band are obtained and the sums of lower order IMFs are used as new features for classification with SVM. ...
This paper presents two different approaches for improved hyperspectral image classification making use of EMD. ...
Landgrebe for providing the Indian Pine and DC Mall data sets [1] , [20] and A. Linderhed for the 2-D-EMD-SVM software. ...
doi:10.1109/tgrs.2010.2070510
fatcat:kfsgs6nbv5dnxjdkyvr3zl5o4m
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020 5832-
5847
Discrete wavelet transforms
Polarimetric SAR Image Semantic Segmentation With 3D Discrete Wavelet
Transform and Markov Random Field. ...
Wen, X., +, TIP 2020 8855-8869 Polarimetric SAR Image Semantic Segmentation With 3D Discrete Wavelet Transform and Markov Random Field. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Dimension Reduction and Remote Sensing Using Modern Harmonic Analysis
[chapter]
2013
Handbook of Geomathematics
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Kernel methods in remote sensing have also been combined with other mathematical techniques, such as Randomized Anisotropic Transforms, [37] , or approximate graph constructions and randomized projections ...
Random projections The notion of using a random projection for dimensionality reduction is not new. In fact, it can be traced back to long before the present wave of interest in CS. ...
doi:10.1007/978-3-642-27793-1_50-1
fatcat:n76iatxmzva4xgruscdt6tw2iq
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