Hyperspectral Image Classification using SVM with PCA

K, Mounika; Aravind, K U; Yamini, M.; Navyasri, P.; Dash, Satyabrata; Suryanarayana, Vadhri

doi:10.1109/ispcc53510.2021.9609461

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…According to the simulation data, CNN + MNF outperformed with maximum accuracy percentage within less time for all datasets. The overall accuracy 35,36 refers to the percentage of correctly predicted sample points compared with the total number of samples. As shown in the following equations, the parameters like specificity, recall, precision, and F1_score [Eqs.…”

Section: Simulation Results and Discussionmentioning

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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Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The overall accuracy 35 , 36 refers to the percentage of correctly predicted sample points compared with the total number of samples. As shown in the following equations, the parameters like specificity, recall, precision, and F1_score [Eqs.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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

et al. 2022

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“…We analyzed the impact of different dimensionality reduction methods on the accuracy of the proposed model, including PCA, Segmented PCA, MNF, Segmented MNF, Bg-MNF, NMF, and the dimensionality reduction method proposed in this paper. For the purpose of comparative analysis, we used state-of-the-art methods such as SVM [44], 3D CNN [27], Fast 3D CNN [28], HybridSN [29], SpectralNET [41], Hybrid 3D 2D CNN [43], etc. Our proposed approach demonstrates superior performance compared to other dimensional-ity reduction methods when evaluated on these deep learning model.…”

Section: Classification Performancementioning

confidence: 99%

A Deep Learning-Based Hyperspectral Object Classification Approach via Imbalanced Training Samples Handling

Islam

Uddin

et al. 2023

Remote Sensing

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Object classification in hyperspectral images involves accurately categorizing objects based on their spectral characteristics. However, the high dimensionality of hyperspectral data and class imbalance pose significant challenges to object classification performance. To address these challenges, we propose a framework that incorporates dimensionality reduction and re-sampling as preprocessing steps for a deep learning model. Our framework employs a novel subgroup-based dimensionality reduction technique to extract and select the most informative features with minimal redundancy. Additionally, the data are resampled to achieve class balance across all categories. The reduced and balanced data are then processed through a hybrid CNN model, which combines a 3D learning block and a 2D learning block to extract spectral–spatial features and achieve satisfactory classification accuracy. By adopting this hybrid approach, we simplify the model while improving performance in the presence of noise and limited sample size. We evaluated our proposed model on the Salinas scene, Pavia University, and Kennedy Space Center benchmark hyperspectral datasets, comparing it to state-of-the-art methods. Our object classification technique achieves highly promising results, with overall accuracies of 99.98%, 99.94%, and 99.46% on the three datasets, respectively. This proposed approach offers a compelling solution to overcome the challenges of high dimensionality and class imbalance in hyperspectral object classification.

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“…Thus, many researches presented various Dimensionality Reduction (DR) methods to overcome the curse of dimensionality of HSI data while preserving the same spatial information. Mounika et al [5] introduced Principal Components Analysis (PCA) to reduce the dimensionality of the HSI images by eliminating the noise in the dataset prior to the classification using Support Vector Machine (SVM). The researchers in [6] used mean shift clustering to combine the spatial and spectral information and then classified the HSI by using a pseudo supervised fusion technique.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction Techniques with Hydranet Framework for HSI Classification

Alkhatib

Al-Saad

Aburaed

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Hyperspectral Imagery (HSI) classification is an important research area in remote sensing community due to its high efficiency in accurately analyzing ground features by assigning a class label to each pixel. This paper explores the use of Band Subset selection (BSS) methods as Dimensionality Reduction (DR) pre-processing stage for HSI classification, and compares them to Principal Component Analysis (PCA) approach. BSS is the problem of selecting the most independent bands in HSI cube. Classification is then performed using a proposed multi-branch HydraNet model that combines 1D, 2D, and 3D convolution. HydraNet is trained and tested using the benchmark Pavia University dataset, and the results are evaluated using Kappa and Overall Accuracy. Experimental results show positive indications of the network's performance, especially when compared to other state-of-the-art CNN networks.

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Hyperspectral Image Classification using SVM with PCA

Cited by 9 publications

References 27 publications

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

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

A Deep Learning-Based Hyperspectral Object Classification Approach via Imbalanced Training Samples Handling

Dimensionality Reduction Techniques with Hydranet Framework for HSI Classification

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