A Novel Deep Learning Framework by Combination of Subspace-Based Feature Extraction and Convolutional Neural Networks for Hyperspectral Images Classification

Alipour-Fard, T.; Arefi, Hossein; Mahmoudi, Somayeh

doi:10.1109/igarss.2018.8518956

Cited by 34 publications

(15 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…On the other hand, 21 feature extraction methods have been reviewed in this study and summarized in Table IV. Among them 7 methods ( [92], [77], [82], [84], [87], [88], and [91]) depended on deep learning (CNN and DNN). CNN and LBP were used in research [92] and achieved high-efficiency accuracy.…”

Section: Sellami and Farahmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

607

210

View full text Add to dashboard Cite

Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

show abstract

Section: Sellami and Farahmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

607

210

View full text Add to dashboard Cite

show abstract

“…However, these methods require manual feature selection, which is subjective and therefore complicates the extraction of high-quality features [12][13][14]. With the development of deep learning [15], increasing numbers of researchers are using neural networks to automatically extract features, thereby eliminating the need for manual feature selection [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

2020

View full text Add to dashboard Cite

Tree species classification is important for the management and sustainable development of forest resources. Traditional object-oriented tree species classification methods, such as support vector machines, require manual feature selection and generally low accuracy, whereas deep learning technology can automatically extract image features to achieve end-to-end classification. Therefore, a tree classification method based on deep learning is proposed in this study. This method combines the semantic segmentation network U-Net and the feature extraction network ResNet into an improved Res-UNet network, where the convolutional layer of the U-Net network is represented by the residual unit of ResNet, and linear interpolation is used instead of deconvolution in each upsampling layer. At the output of the network, conditional random fields are used for post-processing. This network model is used to perform classification experiments on airborne orthophotos of Nanning Gaofeng Forest Farm in Guangxi, China. The results are then compared with those of U-Net and ResNet networks. The proposed method exhibits higher classification accuracy with an overall classification accuracy of 87%. Thus, the proposed model can effectively implement forest tree species classification and provide new opportunities for tree species classification in southern China.

show abstract

“…CNN is a feedforward neural network with a deep structure, which is expert in processing image-related problems [39][40]. The general structure of CNN is shown in Fig.…”

Section: A Cnn Networkmentioning

confidence: 99%

A Hybrid CNN-LSTM Model for Aircraft 4D Trajectory Prediction

Tian

2020

IEEE Access

106

View full text Add to dashboard Cite

The 4D trajectory is a multi-dimensional time series with plentiful spatial-temporal features and has a high degree of complexity and uncertainty. Aiming at these features of aircraft flight trajectory and the problem that it is difficult for existing trajectory prediction methods to extract spatial-temporal features from the trajectory data at the same time, we propose a novel 4D trajectory prediction hybrid architecture based on deep learning, which combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). An 1D convolution is used to extract the spatial dimension feature of the trajectory, and LSTM is used to mine the temporal dimension feature of the trajectory. Hence the highprecision prediction of the 4D trajectory is realized based on the sufficient fusion of the above features. We use real Automatic Dependent Surveillance-Broadcast (ADS-B) historical trajectory data for experiments and compare the proposed method with a single LSTM model and BP model on the same data set. The experimental results show that the trajectory prediction accuracy of the CNN-LSTM hybrid model is superior to a single model. The prediction error is reduced by an average of 21.62% compared to the LSTM model and by an average of 52.45%compared to the BP model. It provides a certain reference for the trajectory prediction research and Air Traffic Management decision-making. INDEX TERMS 4D trajectory prediction, deep learning, CNN-LSTM model, spatial-temporal feature Ma Lan (1966-) Female; Born in Huocheng, Xinjiag Province. Professor. Her main research direction is Air Traffic Information Process, Air Traffic Management Optimization. Shan Tian, born in 1996. Her research interests include data mining and deep learning with recent applications on aircraft 4D trajectory prediction.

show abstract

A Novel Deep Learning Framework by Combination of Subspace-Based Feature Extraction and Convolutional Neural Networks for Hyperspectral Images Classification

Cited by 34 publications

References 16 publications

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

A Hybrid CNN-LSTM Model for Aircraft 4D Trajectory Prediction

Contact Info

Product

Resources

About