Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review

Bera, Somenath; Shrivastava, Vimal K.; Satapathy, Suresh Chandra

doi:10.32604/cmes.2022.020601

Cited by 11 publications

(9 citation statements)

References 103 publications

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“…In addition, to improve the estimation results, a combination scheme based on a mix of the LS and IS data was considered. Investigating modeling methodologies in VNIR enables for more effective use of spectral data to predict soil parameters [63,64].…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

Karray¹,

Elmannai²,

Toumi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Pedo-spectroscopy has the potential to provide valuable information about soil physical, chemical, and biological properties. Nowadays, we may predict soil properties using VNIR field imaging spectra (IS) such as Prisma satellite data or laboratory spectra (LS). The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) for the prediction of several soil properties, including clay, sand, silt, organic matter, nitrate NO3-, and calcium carbonate CaCO 3 , using five VNIR spectra dataset combinations (% IS, % LS) as follows: C1 (0% IS, 100% LS), C2 (20% IS, 80% LS), C3 (50% IS, 50% LS), C4 (80% IS, 20% LS) and C5 (100% IS, 0% LS). Soil samples were collected at bare soils and at the upper (0-30 cm) layer. The data set has been split into a training dataset 80% of the collected data (n = 248) and a validation dataset 20% of the collected data (n = 61). The proposed PLSR and SVR models were trained then tested for each dataset combination. According to our results, SVR outperforms PLSR for both: C1 (0% IS, 100% LS) and C5 (100% IS, 0% LS). For Soil Organic Matter (SOM) prediction, it achieves (R 2 = 0.79%, RMSE = 1.42%) and (R 2 = 0.76%, RMSE = 1.3%), respectively. The data fusion has improved the soil property prediction. The highest improvement was obtained for the SOM property (R 2 = 0.80%, RMSE = 1.39) when using the SVR model and applying the second Combination C2 (20% of IS and 80% LS).

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Section: Discussionmentioning

confidence: 99%

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

Karray¹,

Elmannai²,

Toumi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…Which can extract complex > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < samples by excavating data laws and stacking multi-layer neural structures, which have performed satisfactorily in HSI classification. Semantic segmentation and Convolutional neural networks (CNN) are among the most widely used and influential DL algorithms in images classification [12], [13], [14], [15], [16], [17], [18]. Their proficiency in precisely segmenting and classifying images based on learned features has rendered them indispensable in a wide range of computer vision tasks.…”

Section: Introductionmentioning

confidence: 99%

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

Jiang,

Zhang,

Tang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral remote sensing images with high spatial resolution (H 2 imagery) have an abundant spatial-spectral information, holding tremendous potential for remote sensing fine-grained monitoring and classification. However, challenges such as high spatial heterogeneity, severe intra-class spectral variability and poor signal-to-noise ratio especially in unmanned aerial vehicle (UAV) hyperspectral imagery constrain and hinder the performance of fine-grained classification. Convolutional neural network (CNN) emerges as a formidable and excellent tool for image mining and feature extraction, offering effective utility for land cover classification. In this paper, a parallel convolutional classification network model based on multi-modal filters (including ICA-2D-FPN and SA-3D-CNN branching structures) PCCN-MSS is proposed for precise H 2 imagery classification. The ICA-2D-FPN branch integrates independent component analysis (ICA) into 2D-CNN to extract the multispatial scale and spectral information of H 2 imagery by Feature Pyramid Networks (FPN), meanwhile the SA-3D-CNN branch is designed to extract the spatial and spectral information by combining spectral attention (SA) mechanism and 3D-CNN. Taking hyperspectral imagery of unmanned aerial vehicles containing vegetation and artifactual material ground as an example, the proposed PCCN-MSS model achieves an overall accuracy (OA) of 78.18%, which outperforms by 9.58% than the compared methods. The proposed PCCN-MSS method can mitigate the classification issues of severe salt-and-pepper noise and inaccurate boundary, delivering more satisfactory classification results with robust classification performance and remarkable advantages for H 2 imagery.

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“…The Modified National Institute of Standards and Technology (MNIST) handwritten digit database, one of the most important areas of research in pattern recognition, has excellent research and practical value. Generally speaking, handwriting classification techniques can be divided into either statistical featurerelated methods or structural feature-related approaches [1][2][3][4][5][6][7]. The former is usually caused by features other than the beginning and end points, intersections, contours, and unevenness; whereas the latter is mostly due to the density and feature area of handwritten pointers, and it is easier to mitigate the impact of irregular writing.…”

Section: Introductionmentioning

confidence: 99%

MNIST Handwritten Digit Classification Based on Convolutional Neural Network with Hyperparameter Optimization

Shao¹,

Ma²,

Zhu³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Accurate handwriting recognition has been a challenging computer vision problem, because static feature analysis of the text pictures is often inadequate to account for high variance in handwriting styles across people and poor image quality of the handwritten text. Recently, by introducing machine learning, especially convolutional neural networks (CNNs), the recognition accuracy of various handwriting patterns is steadily improved. In this paper, a deep CNN model is developed to further improve the recognition rate of the MNIST handwritten digit dataset with a fast-converging rate in training. The proposed model comes with a multi-layer deep arrange structure, including 3 convolution and activation layers for feature extraction and 2 fully connected layers (i.e., dense layers) for classification. The model's hyperparameters, such as the batch sizes, kernel sizes, batch normalization, activation function, and learning rate are optimized to enhance the recognition performance. The average classification accuracy of the proposed methodology is found to reach 99.82% on the training dataset and 99.40% on the testing dataset, making it a nearly error-free system for MNIST recognition.

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Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review

Cited by 11 publications

References 103 publications

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

PCCN-MSS: Parallel Convolutional Classification Network Combined Multi-Spatial Scale and Spectral Features for UAV-Borne Hyperspectral With High Spatial Resolution Imagery

MNIST Handwritten Digit Classification Based on Convolutional Neural Network with Hyperparameter Optimization

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