Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images

Sima, Haifeng; Liu, Pei; Liu, Lanlan; Mi, Aizhong; Wang, Jianfang

doi:10.1155/2018/8264961

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…Among them, feature extraction [ 16 – 19 ] minimizes computational complexity by projecting high-dimensional data into low-dimensional data space and feature selection [ 20 ] picks appropriate bands from the original set of spectral bands. Further, a sparse-based method [ 21 ] has been used to derive useful spectral features. Nevertheless, PCA seeks out the best orthogonal vectors for representing information from HSIs [ 22 , 23 ] with minimized spectral dimension (up to 85%).…”

Section: Introductionmentioning

confidence: 99%

[Retracted] A 3D‐2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification

Nyabuga

Song

et al. 2021

Computational Intelligence and Neuroscience

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As one of the fast evolution of remote sensing and spectral imagery techniques, hyperspectral image (HSI) classification has attracted considerable attention in various fields, including land survey, resource monitoring, and among others. Nonetheless, due to a lack of distinctiveness in the hyperspectral pixels of separate classes, there is a recurrent inseparability obstacle in the primary space. Additionally, an open challenge stems from examining efficient techniques that can speedily classify and interpret the spectral-spatial data bands within a more precise computational time. Hence, in this work, we propose a 3D-2D convolutional neural network and transfer learning model where the early layers of the model exploit 3D convolutions to modeling spectral-spatial information. On top of it are 2D convolutional layers to handle semantic abstraction mainly. Toward simplicity and a highly modularized network for image classification, we leverage the ResNeXt-50 block for our model. Furthermore, improving the separability among classes and balance of the interclass and intraclass criteria, we engaged principal component analysis (PCA) for the best orthogonal vectors for representing information from HSIs before feeding to the network. The experimental result shows that our model can efficiently improve the hyperspectral imagery classification, including an instantaneous representation of the spectral-spatial information. Our model evaluation on five publicly available hyperspectral datasets, Indian Pines (IP), Pavia University Scene (PU), Salinas Scene (SA), Botswana (BS), and Kennedy Space Center (KSC), was performed with a high classification accuracy of 99.85%, 99.98%, 100%, 99.82%, and 99.71%, respectively. Quantitative results demonstrated that it outperformed several state-of-the-arts (SOTA), deep neural network-based approaches, and standard classifiers. Thus, it has provided more insight into hyperspectral image classification.

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Section: Introductionmentioning

confidence: 99%

[Retracted] A 3D‐2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification

Nyabuga

Song

et al. 2021

Computational Intelligence and Neuroscience

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“…In Refs. 10, 12–17, supervised, unsupervised, and semisupervised classifications are discussed for HSI data classification purposes. Although the supervised and unsupervised categorization methods mentioned here have their own set of advantages, the application of each approach has its own set of constraints.…”

Section: Related Workmentioning

confidence: 99%

“…The desired class information is well kept in the MFLDA-transformed data, and they can be easily segregated in the low-dimensional space, according to the classification outcome. A sparse representation classification technique 12 was proposed to address the challenges of imprecise context information for HSI by sampling on superpixels.…”

Section: Related Workmentioning

confidence: 99%

“…8,9 Different feature reduction techniques for feature extraction and selection, such as principal component analysis (PCA) 9,10 and linear discriminant analysis (LDA), 11 are two examples of this type of study. Based on the classification process, HSI classification consists of three categories: supervised classification, 12,13 semisupervised, [14][15][16] and unsupervised classification. 17 In the supervised classification, the pixel is labeled according to the least distance constraint for final classification based on the combined sparse coefficients and structured dictionary.…”

Section: Introductionmentioning

confidence: 99%

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Feature extraction and classification of hyperspectral imaging using minimum noise fraction and deep convolutional neural network

et al. 2022

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Remote sensing technology is constantly developing, which has greatly expanded the use of hyperspectral imaging (HSI). In the arena of remote sensing, the classification of HSI has become a challenging topic. The unique properties of hyperspectral data make accurate categorization difficult. In recent years, deep structured learning has emerged as an effective feature extraction technique for effectively addressing nonlinear problems. It is now extensively used to solve a variety of image processing problems. Deep learning is used to classify images and has shown good performance in recent years. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on HSI categorization utilizing several machine learning approaches, such as support vector machine, K-nearest neighbor, and convolutional neural network (CNN). To reduce the number of superfluous and noisy bands in the dataset, principal component analysis and minimum noise fraction (MNF) were utilized. Different performance evaluation measures, such as time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and G-mean, were taken to prove the efficacy of the models. The simulation results show that the combination of MNF and CNN produces better classification accuracy compared with the other considered models.

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Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging

Dash,

Chakravarty,

Giri

et al. 2024

Int J Comput Intell Syst

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In recent years, deep learning techniques have presented a major role in hyperspectral image (HSI) classification. Most commonly Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has greatly advanced the accuracy of hyperspectral image classification, making it powerful tool for remote sensing applications. Deep structure learning, which involves multiple layers of neural network, has shown promising results in effectively addressing nonlinear problems and improving classification accuracy and reduce execution time. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on hyperspectral image categorization utilizing several machine learning approaches such as support vector machine (SVM), K-Nearest Neighbour (KNN), CNN and LSTM. To reduce the number of superfluous and noisy bands in the dataset, Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) were utilized. Different performance evaluation measures like time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and Gmean have been taken to prove the efficacy of the models. Based on the simulation results, it is observed that the LSTM model outperforms the other models in terms of accuracy percentage and time consumption, making it the most effective model for classifying hyperspectral imaging datasets.

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Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images

Cited by 9 publications

References 36 publications

[Retracted] A 3D‐2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification

[Retracted] A 3D‐2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification

Feature extraction and classification of hyperspectral imaging using minimum noise fraction and deep convolutional neural network

Minimum Noise Fraction and Long Short-Term Memory Model for Hyperspectral Imaging

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