Dimensionality reduction using deep belief network in big data case study: Hyperspectral image classification

Arsa, Dewa Made Sri; Jati, Grafika; Mantau, Aprinaldi Jasa; Wasito, Ito

doi:10.1109/iwbis.2016.7872892

Cited by 16 publications

(5 citation statements)

References 13 publications

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“…Furthermore, techniques like Deep Recurrent Neural Networks [47], 3D deep learning frameworks [48], [49], [50], [51], Cascaded Recurrent Neural Networks [31], and Multi-Layer Perceptrons (MLP) [46] emerged as prominent contenders in HSI classification. Hybrid approaches such as Spiking Neural Networks (SNN) [52], 1D CNN [28], Morphological Convolutional Neural Networks (MCNN) [30], S2GraphSage [29], RLSBSA [26], and 3DHyperGamo [27] models have broken previous accuracy records in predicting HSI classes.…”

Section: Convolution Neural Networkmentioning

confidence: 99%

Deep Feature Segmentation Model Driven by Hybrid Convolution Network for Hyper Spectral Image Classification

K Ghotekar,

Shaw,

Rout

2024

IJCDS

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Hyper-spectral image (HSI) classification can support different applications, such as agriculture, military, city planning, land utilization, and identifying distinct regions. It is treated as a crucial topic in the research community. Recent advancements in convolution neural networks (CNN) have shown the unique capability of extracting meaningful features and classification. However, CNN works with square images with fixed dimensions and cannot extract local information of images having distinct geometric variations with context and content relationships; hence, there is a scope for improvement in correctly identifying class boundaries. Encouraged by the facts, we propose an HSI feature segmentation model by the hybrid convolution network (GCNN-RESNET152) for the HSI classification. First, pre-trained CNN on ImageNet is used to obtain the multi-layer feature. Second, the 3D discrete wavelet transform image is fed into the graph convolution network GCN model to gain patch-to-patch correlations feature maps. Finally, the features are integrated using the concatenation method of the three-weighted coefficients. Finally, the linear classifier is used to predict the semantic classes of pixel HSI. The proposed model is tested on four benchmark datasets: Houston University (HU), Indian Pines (IP), Kennedy Space Station (KSS), and Pavia University (PU). The result is compared with state-of-art algorithms and is superior in terms of overall, average, and kappa accuracy.

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Section: Convolution Neural Networkmentioning

confidence: 99%

Deep Feature Segmentation Model Driven by Hybrid Convolution Network for Hyper Spectral Image Classification

K Ghotekar,

Shaw,

Rout

2024

IJCDS

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“…The size of relational data can be reduced in two ways: either to reduce the number of attributes or to reduce the number of data records. One technique to reduce the number of attributes is dimensionality reduction [14]. One example algorithm for dimensionality reduction is principal component analysis (PCA) [15], which is an expensive process.…”

Section: Techniques For Data Reductionmentioning

confidence: 99%

Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation

Alwajidi¹,

Yang²

2020

Adv. sci. technol. eng. syst. j.

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Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such as decision tree and nearest neighbor search. The proposed method can handle streaming data efficiently and, for entropy discretization, provide sufficient information to find the optimal split variable and the optimal split value.

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“…The deep belief network has a deep architecture that can represent multiple features of input patterns hierarchically with the pre-trained restricted Boltzmann machine (RBM). It has been widely used in many fields, such as image processing [15], dimensionality reduction [16], and classification tasks [17]. Previous research has shown that a DBN performs significantly better than shallow neural networks [18].…”

Section: Hybrid Modelmentioning

confidence: 99%

Short-Term Load Forecasting Using a Novel Deep Learning Framework

et al. 2018

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Short-term load forecasting is the basis of power system operation and analysis. In recent years, the use of a deep belief network (DBN) for short-term load forecasting has become increasingly popular. In this study, a novel deep-learning framework based on a restricted Boltzmann machine (RBM) and an Elman neural network is presented. This novel framework is used for short-term load forecasting based on the historical power load data of a town in the UK. The obtained results are compared with an individual use of a DBN and Elman neural network. The experimental results demonstrate that our proposed model can significantly ameliorate the prediction accuracy.

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Dimensionality reduction using deep belief network in big data case study: Hyperspectral image classification

Cited by 16 publications

References 13 publications

Deep Feature Segmentation Model Driven by Hybrid Convolution Network for Hyper Spectral Image Classification

Deep Feature Segmentation Model Driven by Hybrid Convolution Network for Hyper Spectral Image Classification

Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation

Short-Term Load Forecasting Using a Novel Deep Learning Framework

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