scholarly journals A Robust Dynamic Classifier Selection Approach for Hyperspectral Images with Imprecise Label Information

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5262
Author(s):  
Meizhu Li ◽  
Shaoguang Huang ◽  
Jasper De Bock ◽  
Gert de Cooman ◽  
Aleksandra Pižurica
Keyword(s):  
Hyperspectral Image ◽  
Probability Distributions ◽  
Imprecise Probabilities ◽  
Data Sets ◽  
Classifier Selection ◽  
Training Samples ◽  
Proposed Model ◽  
Label Information ◽  
Dynamic Classifier Selection ◽  
The Individual

Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors in data labels. Despite the growing importance of this aspect, it has not been sufficiently studied in the literature yet. In this paper, we analyze the effect of erroneous sample labels on probability distributions of the principal components of HSIs, and provide in this way a statistical analysis of the resulting uncertainty in classifiers. Building on the theory of imprecise probabilities, we develop a novel robust dynamic classifier selection (R-DCS) model for data classification with erroneous labels. Particularly, spectral and spatial features are extracted from HSIs to construct two individual classifiers for the dynamic selection, respectively. The proposed R-DCS model is based on the robustness of the classifiers’ predictions: the extent to which a classifier can be altered without changing its prediction. We provide three possible selection strategies for the proposed model with different computational complexities and apply them on three benchmark data sets. Experimental results demonstrate that the proposed model outperforms the individual classifiers it selects from and is more robust to errors in labels compared to widely adopted approaches.

scholarly journals An Influence Prediction Model for Microblog Entries on Public Health Emergencies

2019 ◽  
Vol 3 (2) ◽  
pp. 102-115 ◽  
Author(s):  
Lu An ◽  
Xingyue Yi ◽  
Yuxin Han ◽  
Gang Li
Keyword(s):  
Public Health ◽  
Prediction Model ◽  
Latent Dirichlet Allocation ◽  
Prediction Models ◽  
Data Sets ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Public Health Emergencies ◽  
Proposed Model ◽  
The Individual

Abstract This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

scholarly journals A Dual-Path Small Convolution Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (17) ◽  
pp. 3411
Author(s):  
Lanxue Dang ◽  
Peidong Pang ◽  
Xianyu Zuo ◽  
Yang Liu ◽  
Jay Lee
Keyword(s):  
Hyperspectral Image ◽  
Middle Layer ◽  
Hyperspectral Data ◽  
Model Parameters ◽  
Data Sets ◽  
Testing Time ◽  
Spatial Features ◽  
Current State ◽  
Proposed Model ◽  
Fully Connected

Convolutional neural network (CNN) has shown excellent performance in hyperspectral image (HSI) classification. However, the structure of the CNN models is complex, requiring many training parameters and floating-point operations (FLOPs). This is often inefficient and results in longer training and testing time. In addition, the label samples of hyperspectral data are limited, and a deep network often causes the over-fitting phenomenon. Hence, a dual-path small convolution (DPSC) module is proposed. It is composed of two 1 × 1 small convolutions with a residual path and a density path. It can effectively extract abstract features from HSI. A dual-path small convolution network (DPSCN) is constructed by stacking DPSC modules. Specifically, the proposed model uses a DPSC module to complete the extraction of spectral and spectral–spatial features successively. It then uses a global average pooling layer at the end of the model to replace the conventional fully connected layer to complete the final classification. In the implemented study, all convolutional layers of the proposed network, except the middle layer, use 1 × 1 small convolution, effectively reduced model parameters and increased the speed of feature extraction processes. DPSCN was compared with several current state-of-the-art models. The results on three benchmark HSI data sets demonstrated that the proposed model is of lower complexity, has stronger generalization ability, and has higher classification efficiency.

An Influence Prediction Model for Microblog Entries on Public Health Emergencies

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Lu An ◽  
Xingyue Yi ◽  
Yuxin Han ◽  
Gang Li
Keyword(s):  
Public Health ◽  
Prediction Model ◽  
Latent Dirichlet Allocation ◽  
Prediction Models ◽  
Data Sets ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Public Health Emergencies ◽  
Proposed Model ◽  
The Individual

Abstract This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

scholarly journals Spatial–Spectral Feature Refinement for Hyperspectral Image Classification Based on Attention-Dense 3D-2D-CNN

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5191
Author(s):  
Jin Zhang ◽  
Fengyuan Wei ◽  
Fan Feng ◽  
Chunyang Wang
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Small Sample ◽  
Training Data ◽  
Spectral Features ◽  
Training Samples ◽  
Proposed Model ◽  
Limited Training Samples ◽  
Hyperspectral Classification

Convolutional neural networks provide an ideal solution for hyperspectral image (HSI) classification. However, the classification effect is not satisfactory when limited training samples are available. Focused on “small sample” hyperspectral classification, we proposed a novel 3D-2D-convolutional neural network (CNN) model named AD-HybridSN (Attention-Dense-HybridSN). In our proposed model, a dense block was used to reuse shallow features and aimed at better exploiting hierarchical spatial–spectral features. Subsequent depth separable convolutional layers were used to discriminate the spatial information. Further refinement of spatial–spectral features was realized by the channel attention method and spatial attention method, which were performed behind every 3D convolutional layer and every 2D convolutional layer, respectively. Experiment results indicate that our proposed model can learn more discriminative spatial–spectral features using very few training data. In Indian Pines, Salinas and the University of Pavia, AD-HybridSN obtain 97.02%, 99.59% and 98.32% overall accuracy using only 5%, 1% and 1% labeled data for training, respectively, which are far better than all the contrast models.

scholarly journals Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification

2019 ◽  
Vol 11 (9) ◽  
pp. 1116 ◽  
Author(s):  
Qingming Leng ◽  
Haiou Yang ◽  
Junjun Jiang
Keyword(s):  
Image Classification ◽  
Transfer Matrix ◽  
Hyperspectral Image ◽  
Homogeneous Region ◽  
Practical Case ◽  
Sparse Graph ◽  
Hyperspectral Image Classification ◽  
Label Noise ◽  
Training Samples ◽  
Label Information

In a real hyperspectral image classification task, label noise inevitably exists in training samples. To deal with label noise, current methods assume that noise obeys the Gaussian distribution, which is not the real case in practice, because in most cases, we are more likely to misclassify training samples at the boundaries between different classes. In this paper, we propose a spectral–spatial sparse graph-based adaptive label propagation (SALP) algorithm to address a more practical case, where the label information is contaminated by random noise and boundary noise. Specifically, the SALP mainly includes two steps: First, a spectral–spatial sparse graph is constructed to depict the contextual correlations between pixels within the same superpixel homogeneous region, which are generated by superpixel image segmentation, and then a transfer matrix is produced to describe the transition probability between pixels. Second, after randomly splitting training pixels into “clean” and “polluted,” we iteratively propagate the label information from “clean” to “polluted” based on the transfer matrix, and the relabeling strategy for each pixel is adaptively adjusted along with its spatial position in the corresponding homogeneous region. Experimental results on two standard hyperspectral image datasets show that the proposed SALP over four major classifiers can significantly decrease the influence of noisy labels, and our method achieves better performance compared with the baselines.

scholarly journals Active Semi-Supervised Random Forest for Hyperspectral Image Classification

2019 ◽  
Vol 11 (24) ◽  
pp. 2974 ◽  
Author(s):  
Youqiang Zhang ◽  
Guo Cao ◽  
Xuesong Li ◽  
Bisheng Wang ◽  
Peng Fu
Keyword(s):  
Random Forest ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Great Success ◽  
Full Potential ◽  
Hyperspectral Image Classification ◽  
Unified Framework ◽  
Proposed Model

Random forest (RF) has obtained great success in hyperspectral image (HSI) classification. However, RF cannot leverage its full potential in the case of limited labeled samples. To address this issue, we propose a unified framework that embeds active learning (AL) and semi-supervised learning (SSL) into RF (ASSRF). Our aim is to utilize AL and SSL simultaneously to improve the performance of RF. The objective of the proposed method is to use a small number of manually labeled samples to train classifiers with relative high classification accuracy. To achieve this goal, a new query function is designed to query the most informative samples for manual labeling, and a new pseudolabeling strategy is introduced to select some samples for pseudolabeling. Compared with other AL- and SSL-based methods, the proposed method has several advantages. First, ASSRF utilizes the spatial information to construct a query function for AL, which can select more informative samples. Second, in addition to providing more labeled samples for SSL, the proposed pseudolabeling method avoids bias caused by AL-labeled samples. Finally, the proposed model retains the advantages of RF. To demonstrate the effectiveness of ASSRF, we conducted experiments on three real hyperspectral data sets. The experimental results have shown that our proposed method outperforms other state-of-the-art methods.

Semi-Supervised Classification for Hyperspectral Image Based on Tri-Training

2014 ◽  
Vol 687-691 ◽  
pp. 3644-3647 ◽  
Author(s):  
Li Guo Wang ◽  
Yue Shuang Yang ◽  
Ting Ting Lu
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Image ◽  
Learning Algorithm ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Hyperspectral Image Classification ◽  
Training Set ◽  
De Algorithm ◽  
Training Samples ◽  
Limited Training Samples

Hyperspectral image classification is difficult due to the high dimensional features but limited training samples. Tri-training learning is a widely used semi-supervised classification method that addresses the problem of lacking of labeled examples. In this paper, a novel semi-supervised learning algorithm based on tri-training method is proposed. The proposed algorithm combines margin sampling (MS) technique and differential evolution (DE) algorithm to select the most informative samples and perturb them randomly. Then the samples we obtained, which can fulfill the labeled data distribution and introduce diversity to multiple classifiers, are added to training set to train base classifiers for tri-training. The proposed algorithm is experimentally validated using real hyperspectral data sets, indicating that the combination of MS and DE can significantly reduce the need of labeled samples while achieving high accuracy compared with state-of-the-art algorithms.

scholarly journals A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification

2019 ◽  
Vol 11 (11) ◽  
pp. 1325 ◽  
Author(s):  
Chen Chen ◽  
Yi Ma ◽  
Guangbo Ren
Keyword(s):  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Training Sample ◽  
Practical Applications ◽  
Model Based ◽  
Training Samples ◽  
Proposed Model ◽  
Convergence Performance ◽  
Hyperspectral Classification

Deep learning models, especially the convolutional neural networks (CNNs), are very active in hyperspectral remote sensing image classification. In order to better apply the CNN model to hyperspectral classification, we propose a CNN model based on Fletcher–Reeves algorithm (F–R CNN), which uses the Fletcher–Reeves (F–R) algorithm for gradient updating to optimize the convergence performance of the model in classification. In view of the fact that there are fewer optional training samples in practical applications, we further propose a method of increasing the number of samples by adding a certain degree of perturbed samples, which can also test the anti-interference ability of classification methods. Furthermore, we analyze the anti-interference and convergence performance of the proposed model in terms of different training sample data sets, different batch training sample numbers and iteration time. In this paper, we describe the experimental process in detail and comprehensively evaluate the proposed model based on the classification of CHRIS hyperspectral imagery covering coastal wetlands, and further evaluate it on a commonly used hyperspectral image benchmark dataset. The experimental results show that the accuracy of the two models after increasing training samples and adjusting the number of batch training samples is improved. When the number of batch training samples is continuously increased to 350, the classification accuracy of the proposed method can still be maintained above 80.7%, which is 2.9% higher than the traditional one. And its time consumption is less than that of the traditional one while ensuring classification accuracy. It can be concluded that the proposed method has anti-interference ability and outperforms the traditional CNN in terms of batch computing adaptability and convergence speed.

An efficient superpixel-based sparse representation framework for hyperspectral image classification

2017 ◽  
Vol 15 (06) ◽  
pp. 1750061 ◽  
Author(s):  
Sen Jia ◽  
Bin Deng ◽  
Qiang Huang
Keyword(s):  
Sparse Representation ◽  
Visual Recognition ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Spectral Characteristics ◽  
Small Region ◽  
Data Sets ◽  
Training Samples ◽  
The Rich ◽  
Highly Correlated

As a powerful classifier, sparse representation-based classification (SRC) has successfully been applied in various visual recognition problems. However, due to the highly correlated bands and insufficient training samples of hyperspectral image (HSI) data, it still remains a challenging problem to effectively apply SRC in HSI. Considering the rich information of spatial structure of materials in HSI, that means the adjacent pixels belong to the same class with a high probability, in this paper, we propose an efficient superpixel-based sparse representation framework for HSI classification. Each superpixel can be regarded as a small region consisting of a number of pixels with similar spectral characteristics. The proposed framework utilizes superpixel to exploit spatial information which can greatly improve classification accuracy. Specifically, SRC is firstly used to classify the HSI data. Meanwhile, an efficient segmentation algorithm is applied to divide the HSI into many disjoint superpixels. Then, each superpixel is used to fuse the SRC classification results in superpixel level. Experimental results on two real-world HSI data sets have shown that the proposed superpixel-based SRC (SP-SRC) framework has a significant improvement over the pixel-based SRC method.

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