Deep proximal support vector machine classifiers for hyperspectral images classification

Kalaiarasi, G.; Maheswari, S. Uma

doi:10.1007/s00521-021-05965-0

Cited by 16 publications

(8 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the proposed model uses a pairwise matrix to distinguish between shadow elements with crop features. Further, the existing model deep learning model (29,30) , uses SVM for building decision boundary; however, these model considers hard decision boundary. As a result, when there no enough training data higher is classification occurs.…”

Section: Resultsmentioning

confidence: 99%

“…However, achieve very poor classification accuracy especially for the crop with less feature; To address class imbalance issues number of Deep learning-based classification models has been emphasized in recent times (18)(19)(20)(21)(22) . However, most of the existing deep learning-based HSI object classification model (18,(23)(24)(25)(26)(27)(28) achieves very good classification accuracies but induces more computation overhead; Further, this model requires a high number of features for training model and classification model are designed considering hard decision boundaries; To overcome these issues, this work employs improved decision boundary (IDB) by considering soft-margin for SVM (29,30) . The SSF trained using IDBSVM aided in overcoming class imbalance issues, reducing misclassification, and improving classification accuracies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial-Spectral Feature for Extraction Technique for Hyperspectral Crop Classification

Vani¹,

Thippeswamy²

2022

IJST

View full text Add to dashboard Cite

Objectives:To present an extraction technique for the classification of the hyperspectral crop using the spatial-spectral feature. Methods: This paper presents a spatial-spectral feature extraction method employing the Image fusion technique and intrinsic feature extraction and a model for Improved Decision Boundary (IDB) using Support Vector Machine (SVM). Findings:The experiments have been conducted by using the Indian pines dataset which was extracted using the AVIRIS sensor. The dataset comprises of 16 distinctive classes such as corn, wheat, oats etc, which have used for evaluation of our model. Before the evaluation of the dataset the model has been trained using different training datasets in order to increase the accuracy and reduce misclassification. Moreover, the Spatial-Spectral Feature (SSF) model aided in distinguishing between crop intrinsic features and shadow element under dynamic environment condition. Our model attained 99.54%, 99.4%, 99.25% and 9.8 sec for OA accuracy, AA accuracy, Kappa and Time respectively. Furthermore, the overall accuracy of the model for the Support Vector Machine-3-dimensional discrete wavelet transform (SVM-3DDWT), Convolutional Neural Network and Spatial-Spectral Feature Extraction Technique showed result of 94.28%, 96.12% and 99.4% respectively. Other existing models showed a low accuracy for the same dataset. Further, for addressing class imbalance issues this work modelled an improved decision boundary model for SVM by considering soft-margin rather than hard margin. The SSF-IDBSVM model achieves much better accuracies with less misclassification in comparison with recent deep learning-based HSI classification model. Novelty: Recently, several feature extraction and deep learning-based crop classification model have been modelled. However, existing crop classification fails to distinguish crop intrinsic feature concerning shadow feature; further, consider hard decision boundary; as a result, high misclassification is induced for smaller class size. Hence, this study provides an extraction feature which provides the classification of the crop in less time with higher classification and less misclassification.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial-Spectral Feature for Extraction Technique for Hyperspectral Crop Classification

Vani¹,

Thippeswamy²

2022

IJST

View full text Add to dashboard Cite

show abstract

“…The research hypothesis the problems that existing SVMbased Hyperspectral object classification [18] are modeled using hard margin decision boundary [19], [20]; thus, high induce misclassification for smaller classes. Thus, are not efficient when data is imbalanced and induce high computational overheads.…”

Section: Literature Surveymentioning

confidence: 99%

“…Thus, for retaining spectral features more efficiently Image fusion (IF) methodologies are used in recent work. However, IF-based methodologies achieve poor classification performance; this is because they are affected due to the presence of noise and www.ijacsa.thesai.org Recently, Deep Learning (DL) methodologies [15], [16] have been adopted for HSI crop classification [17], [18] with good accuracies [19], [20] which is studied in literature survey section. However, these DL-based methodologies induce high computation overhead and require a higher number of training parameters [21].…”

Section: Introductionmentioning

confidence: 99%

“…Then we present an inherent feature extraction (IFE) method for distinguishing physical properties and shading elements of different crops. Existing models are trained using a Support vector machine (SVM) [18] for performing crop classification; the classification accuracies using SVM are affected due to misclassification [19]; especially when data is imbalanced and two objects exhibit similar physical features [20]. Thus, to address data imbalance issues and reduce misclassification in this we introduced a soft-margin decision boundary optimization model for SVM.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inherent Feature Extraction and Soft Margin Decision Boundary Optimization Technique for Hyperspectral Crop Classification

Babu¹,

C²

2021

IJACSA

View full text Add to dashboard Cite

Crop productivity and disaster management can be enhanced by employing hyperspectral images. Hyperspectral imaging is widely utilized in identifying and classifying objects on the ground surface for various agriculture application uses such as crop mapping, flood management, identifying crops damaged due to flood/drought, etc. Hyperspectral imaging-based crop classification is a very challenging task because of spectral dimensions and poor spatial feature representation. Designing efficient feature extraction and dimension reduction techniques can address high dimensionality problems. Nonetheless, achieving good classification accuracies with minimal computation overhead is a challenging task in Hyperspectral imaging-based crop classification. In meeting research challenges, this work presents Hyperspectral imaging-based crop classification using soft-margin decision boundary optimization (SMDBO) based Support Vector Machine (SVM) along with Image Fusion-Recursive Filter (IFRF) and Inherent Feature Extraction (IFE). In this work, IFRF is used for reducing spectral features with meaningful representation. Then, IFE is used for differentiating physical properties and shading elements of different objects spatially. Both spectral and spatial features extracted are trained using SMDBO-SVM for performing hyperspectral object classification. Using SMDBO-SVM for Hyperspectral object classification aid in addressing class imbalance issues; thus, the proposed IFE-SMDBO-SVM model achieves better accuracies and with minimal misclassification in comparison with state-of-art statistical and Deep Learning (DL) based Hyperspectral object classification model using standard dataset Indian Pines and Pavia University.

show abstract

A unified kernel sparse representation framework for supervised learning problems

Ye,

Yang,

Zhu

et al. 2023

Neural Comput & Applic

View full text Add to dashboard Cite

Deep proximal support vector machine classifiers for hyperspectral images classification

Cited by 16 publications

References 37 publications

Spatial-Spectral Feature for Extraction Technique for Hyperspectral Crop Classification

Spatial-Spectral Feature for Extraction Technique for Hyperspectral Crop Classification

Inherent Feature Extraction and Soft Margin Decision Boundary Optimization Technique for Hyperspectral Crop Classification

A unified kernel sparse representation framework for supervised learning problems

Contact Info

Product

Resources

About