scholarly journals Unsupervised Multi-Level Feature Extraction for Improvement of Hyperspectral Classification

2021 ◽  
Vol 13 (8) ◽  
pp. 1602
Author(s):  
Qiaoqiao Sun ◽  
Xuefeng Liu ◽  
Salah Bourennane
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Great Promise ◽  
Learning Models ◽  
Single Level ◽  
Multiple Networks ◽  
Multi Level ◽  
Hyperspectral Classification

Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features.

scholarly journals Evaluating the informativeness of deep learning annotations for human complex diseases

2019 ◽  
Author(s):  
Kushal K. Dey ◽  
Bryce Van de Geijn ◽  
Samuel Sungil Kim ◽  
Farhad Hormozdiari ◽  
David R. Kelley ◽  
...  
Keyword(s):  
Deep Learning ◽  
Complex Traits ◽  
Complex Disease ◽  
Complex Diseases ◽  
Great Promise ◽  
Full Potential ◽  
Learning Models ◽  
Allelic Effect ◽  
Meta Analyses ◽  
Human Complex

AbstractDeep learning models have shown great promise in predicting genome-wide regulatory effects from DNA sequence, but their informativeness for human complex diseases and traits is not fully understood. Here, we evaluate the disease informativeness of allelic-effect annotations (absolute value of the predicted difference between reference and variant alleles) constructed using two previously trained deep learning models, DeepSEA and Basenji. We apply stratified LD score regression (S-LDSC) to 41 independent diseases and complex traits (average N=320K) to evaluate each annotation’s informativeness for disease heritability conditional on a broad set of coding, conserved, regulatory and LD-related annotations from the baseline-LD model and other sources; as a secondary metric, we also evaluate the accuracy of models that incorporate deep learning annotations in predicting disease-associated or fine-mapped SNPs. We aggregated annotations across all tissues (resp. blood cell types or brain tissues) in meta-analyses across all 41 traits (resp. 11 blood-related traits or 8 brain-related traits). These allelic-effect annotations were highly enriched for disease heritability, but produced only limited conditionally significant results – only Basenji-H3K4me3 in meta-analyses across all 41 traits and brain-specific Basenji-H3K4me3 in meta-analyses across 8 brain-related traits. We conclude that deep learning models are yet to achieve their full potential to provide considerable amount of unique information for complex disease, and that the informativeness of deep learning models for disease beyond established functional annotations cannot be inferred from metrics based on their accuracy in predicting regulatory annotations.

scholarly journals Challenges of Sarcasm Detection for Social Network : A Literature Review

2020 ◽  
Vol 8 (2) ◽  
pp. 169
Author(s):  
Afiyati Afiyati ◽  
Azhari Azhari ◽  
Anny Kartika Sari ◽  
Abdul Karim
Keyword(s):  
Social Media ◽  
Feature Extraction ◽  
Deep Learning ◽  
Contextual Information ◽  
Unstructured Data ◽  
Learning Models ◽  
Critical Aspect ◽  
Hybrid Approaches ◽  
Data Problem ◽  
The Future

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance.

scholarly journals Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification

2020 ◽  
Vol 12 (2) ◽  
pp. 280 ◽  
Author(s):  
Liqin Liu ◽  
Zhenwei Shi ◽  
Bin Pan ◽  
Ning Zhang ◽  
Huanlin Luo ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Hyperspectral Data ◽  
Learning Technology ◽  
Learning Networks ◽  
Spatial Features ◽  
Training Samples ◽  
Rgb Image

In recent years, deep learning technology has been widely used in the field of hyperspectral image classification and achieved good performance. However, deep learning networks need a large amount of training samples, which conflicts with the limited labeled samples of hyperspectral images. Traditional deep networks usually construct each pixel as a subject, ignoring the integrity of the hyperspectral data and the methods based on feature extraction are likely to lose the edge information which plays a crucial role in the pixel-level classification. To overcome the limit of annotation samples, we propose a new three-channel image build method (virtual RGB image) by which the trained networks on natural images are used to extract the spatial features. Through the trained network, the hyperspectral data are disposed as a whole. Meanwhile, we propose a multiscale feature fusion method to combine both the detailed and semantic characteristics, thus promoting the accuracy of classification. Experiments show that the proposed method can achieve ideal results better than the state-of-art methods. In addition, the virtual RGB image can be extended to other hyperspectral processing methods that need to use three-channel images.

AUTOMATIC DETECTION OF CARDIOVASCULAR DISEASE USING DEEP KERNEL EXTREME LEARNING MACHINE

2018 ◽  
Vol 30 (06) ◽  
pp. 1850038
Author(s):  
Dongping Li
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Feature Extraction ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Learning Process ◽  
Support Vector ◽  
Learning Models ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

The electrocardiogram (ECG) is a principal signal employed to automatically diagnose cardiovascular disease in shallow and deep learning models. However, ECG feature extraction is required and this may reduce diagnosis accuracy in traditional shallow learning models, while backward propagation (BP) algorithm used by the traditional deep learning models has the disadvantages of local minimization and slow convergence rate. To solve these problems, a new deep learning algorithm called deep kernel extreme learning machine (DKELM) is proposed by combining the extreme learning machine auto-encoder (ELM-AE) and kernel ELM (KELM). In the new DKELM architecture with [Formula: see text] hidden layers, ELM-AEs are employed by the front [Formula: see text] hidden layers for feature extraction in the unsupervised learning process, which can effectively extract abstract features from the original ECG signal. To overcome the “dimension disaster” problem, the kernel function is introduced into ELM to act as classifier by the [Formula: see text]th hidden layer in the supervised learning process. The experiments demonstrate that DKELM outperforms the BP neural network, support vector machine (SVM), extreme learning machine (ELM), deep auto-encoder (DAE), deep belief network (DBN) in classification accuracy. Though the accuracy of convolutional neural network (CNN) is almost the same as DKELM, the computing time of CNN is much longer than DKELM.

scholarly journals Sea Ice Image Classification Based on Heterogeneous Data Fusion and Deep Learning

2021 ◽  
Vol 13 (4) ◽  
pp. 592
Author(s):  
Yanling Han ◽  
Yekun Liu ◽  
Zhonghua Hong ◽  
Yun Zhang ◽  
Shuhu Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Data Fusion ◽  
Sea Ice ◽  
Image Classification ◽  
Heterogeneous Data ◽  
Optical Data ◽  
Spectral Information ◽  
Texture Information ◽  
Multi Level

Sea ice is one of the typical causes of marine disasters. Sea ice image classification is an important component of sea ice detection. Optical data contain rich spectral information, but they do not allow one to easily distinguish between ground objects with a similar spectrum and foreign objects with the same spectrum. Synthetic aperture radar (SAR) data contain rich texture information, but the data usually have a single source. The limitation of single-source data is that they do not allow for further improvements of the accuracy of remote sensing sea ice classification. In this paper, we propose a method for sea ice image classification based on deep learning and heterogeneous data fusion. Utilizing the advantages of convolutional neural networks (CNNs) in terms of depth feature extraction, we designed a deep learning network structure for SAR and optical images and achieve sea ice image classification through feature extraction and a feature-level fusion of heterogeneous data. For the SAR images, the improved spatial pyramid pooling (SPP) network was used and texture information on sea ice at different scales was extracted by depth. For the optical data, multi-level feature information on sea ice such as spatial and spectral information on different types of sea ice was extracted through a path aggregation network (PANet), which enabled low-level features to be fully utilized due to the gradual feature extraction of the convolution neural network. In order to verify the effectiveness of the method, two sets of heterogeneous sentinel satellite data were used for sea ice classification in the Hudson Bay area. The experimental results show that compared with the typical image classification methods and other heterogeneous data fusion methods, the method proposed in this paper fully integrates multi-scale and multi-level texture and spectral information from heterogeneous data and achieves a better classification effect (96.61%, 95.69%).

scholarly journals Fully distributed rainfall-runoff modeling using spatial-temporal graph neural network

2022 ◽  
Author(s):  
Zhongrun Xiang ◽  
Ibrahim Demir
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Spatial Information ◽  
Model Performance ◽  
Grid Cell ◽  
Great Promise ◽  
Rainfall Runoff ◽  
Streamflow Forecasting ◽  
Runoff Modeling ◽  
Runoff Model

Recent studies using latest deep learning algorithms such as LSTM (Long Short-Term Memory) have shown great promise in time-series modeling. There are many studies focusing on the watershed-scale rainfall-runoff modeling or streamflow forecasting, often considering a single watershed with limited generalization capabilities. To improve the model performance, several studies explored an integrated approach by decomposing a large watershed into multiple sub-watersheds with semi-distributed structure. In this study, we propose an innovative physics-informed fully-distributed rainfall-runoff model, NRM-Graph (Neural Runoff Model-Graph), using Graph Neural Networks (GNN) to make full use of spatial information including the flow direction and geographic data. Specifically, we applied a time-series model on each grid cell for its runoff production. The output of each grid cell is then aggregated by a GNN as the final runoff at the watershed outlet. The case study shows that our GNN based model successfully represents the spatial information in predictions. NRM-Graph network has shown less over-fitting and a significant improvement on the model performance compared to the baselines with spatial information. Our research further confirms the importance of spatially distributed hydrological information in rainfall-runoff modeling using deep learning, and we encourage researchers to incorporate more domain knowledge in modeling.

Hyperspectral Data Feature Extraction Using Deep Learning Hybrid Model

2018 ◽  
Vol 102 (4) ◽  
pp. 3529-3543
Author(s):  
Xinhua Jiang ◽  
Heru Xue ◽  
Lina Zhang ◽  
Xiaojing Gao ◽  
Yanqing Zhou ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Hybrid Model ◽  
Hyperspectral Data

scholarly journals A Novel Method of Hyperspectral Data Classification Based on Transfer Learning and Deep Belief Network

2019 ◽  
Vol 9 (7) ◽  
pp. 1379 ◽  
Author(s):  
Ke Li ◽  
Mingju Wang ◽  
Yixin Liu ◽  
Nan Yu ◽  
Wei Lan
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Deep Belief Network ◽  
Learning Method ◽  
Target Domain ◽  
Data Set ◽  
Belief Network ◽  
Target Data

The classification of hyperspectral data using deep learning methods can obtain better results than the previous shallow classifiers, but deep learning algorithms have some limitations. These algorithms require a large amount of data to train the network, while also needing a certain amount of labeled data to fine-tune the network. In this paper, we propose a new hyperspectral data processing method based on transfer learning and the deep learning method. First, we use a hyperspectral data set that is similar to the target data set to pre-train the deep learning network. Then, we use the transfer learning method to find the common features of the source domain data and target domain data. Second, we propose a model structure that combines the deep transfer learning model to utilize a combination of spatial information and spectral information. Using transfer learning, we can obtain the spectral features. Then, we obtain several principal components of the target data. These will be regarded as the spatial features of the target domain data, and we use the joint features for the classifier. The data are obtained from a hyperspectral public database. Using the same amount of data, our method based on transfer learning and deep belief network obtains better classification accuracy in a shorter amount of time.

scholarly journals Semi-Supervised Generative Adversarial Nets with Multiple Generators for SAR Image Recognition

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2706 ◽  
Author(s):  
Fei Gao ◽  
Fei Ma ◽  
Jun Wang ◽  
Jinping Sun ◽  
Erfu Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Performance ◽  
Sar Image ◽  
Learning Models ◽  
Sar Images ◽  
Stationary Target ◽  
Optical Images ◽  
Supervised Methods ◽  
The Stability

As an important model of deep learning, semi-supervised learning models are based on Generative Adversarial Nets (GANs) and have achieved a competitive performance on standard optical images. However, the training of GANs becomes unstable when they are applied to SAR images, which reduces the feature extraction capability of the discriminator in GANs. This paper presents a new semi-supervised GANs with Multiple generators and a classifier (MCGAN). This model improves the stability of training for SAR images by employing multiple generators. A multi-classifier is introduced to the new GANs to utilize the labeled images during the training of the GANs, which shares the low level layers with the discriminator. Then, the layers of the trained discriminator and the classifier construct the recognition network for SAR images after having been finely tuned using a small number of the labeled images. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) databases show that the proposed recognition network achieves a better and more stable recognition performance than several traditional semi-supervised methods as well as other GANs-based semi-supervised methods.

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