Collaborative learning of lightweight convolutional neural network and deep clustering for hyperspectral image semi-supervised classification with limited training samples

2020 ◽  
Vol 161 ◽  
pp. 164-178 ◽  
Author(s):  
Bei Fang ◽  
Ying Li ◽  
Haokui Zhang ◽  
Jonathan Cheung-Wai Chan
Keyword(s):  
Neural Network ◽  
Collaborative Learning ◽  
Convolutional Neural Network ◽  
Supervised Classification ◽  
Hyperspectral Image ◽  
Training Samples ◽  
Limited Training Samples

scholarly journals Semi-Supervised Classification for Hyperspectral Images Based on Multiple Classifiers and Relaxation Strategy

2018 ◽  
Vol 7 (7) ◽  
pp. 284 ◽  
Author(s):  
Fuding Xie ◽  
Dongcui Hu ◽  
Fangfei Li ◽  
Jun Yang ◽  
Deshan Liu
Keyword(s):  
Supervised Classification ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Multinomial Logistic Regression ◽  
Hyperspectral Data ◽  
Classification Result ◽  
Training Samples ◽  
Comparison Results ◽  
Local Mean ◽  
Limited Training Samples

Hyperspectral image (HSI) classification is a fundamental and challenging problem in remote sensing and its various applications. However, it is difficult to perfectly classify remotely sensed hyperspectral data by directly using classification techniques developed in pattern recognition. This is partially owing to a multitude of noise points and the limited training samples. Based on multinomial logistic regression (MLR), the local mean-based pseudo nearest neighbor (LMPNN) rule, and the discontinuity preserving relaxation (DPR) method, in this paper, a semi-supervised method for HSI classification is proposed. In pre-processing and post-processing, the DPR strategy is adopted to denoise the original hyperspectral data and improve the classification accuracy, respectively. The application of two classifiers, MLR and LMPNN, can automatically acquire more labeled samples in terms of a few labeled instances per class. This is termed the pre-classification procedure. The final classification result of the HSI is obtained by employing the MLRsub approach. The effectiveness of the proposal is experimentally evaluated by two real hyperspectral datasets, which are widely used to test the performance of the HSI classification algorithm. The comparison results using several competing methods confirm that the proposed method is effective, even for limited training samples.

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 Extended Morphological Profile Cube for Hyperspectral Image Classification

2021 ◽  
Author(s):  
ALOU DIAKITE ◽  
GUI JIANGSHENG ◽  
FU XIAPING
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Kappa Coefficient ◽  
Sparse Principal Component Analysis ◽  
Hyperspectral Image Classification ◽  
New Approach ◽  
Spatial Features ◽  
Training Samples

<p>Hyperspectral image (HSI) classification using convolutional neural network requires a lot of training samples, which is not always available. Consequently, decreases the classification accuracy due to the overfitting problem. Many studies have been conducted to solve the issue; however, they failed to solve it entirely. Therefore, we proposed a new approach to classify HSI with few training samples using a convolutional neural network in that context. The proposed approach employed an extended morphological profile cube (EMPC) to extract rich spectral-spatial features and then used a 3D densely connected network for classification. Besides, we used sparse principal component analysis to reduce the high spectral dimension of HSI. Experiments results on Indian Pines (IP) and University of Pavia (UP) datasets proved the efficiency of the proposed approach. It increased the OA by 2.61% - 13% and the Kappa coefficient by 2.68% - 15:51% on IP dataset and increased the OA by 0.17% - 11% and the Kappa coefficient by 0.23% - 19% on UP dataset, which is superior to some state-of-art methods.</p>

scholarly journals Extended Morphological Profile Cube for Hyperspectral Image Classification

2021 ◽  
Author(s):  
ALOU DIAKITE ◽  
GUI JIANGSHENG ◽  
FU XIAPING
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Kappa Coefficient ◽  
Sparse Principal Component Analysis ◽  
Hyperspectral Image Classification ◽  
New Approach ◽  
Spatial Features ◽  
Training Samples

<p>Hyperspectral image (HSI) classification using convolutional neural network requires a lot of training samples, which is not always available. Consequently, decreases the classification accuracy due to the overfitting problem. Many studies have been conducted to solve the issue; however, they failed to solve it entirely. Therefore, we proposed a new approach to classify HSI with few training samples using a convolutional neural network in that context. The proposed approach employed an extended morphological profile cube (EMPC) to extract rich spectral-spatial features and then used a 3D densely connected network for classification. Besides, we used sparse principal component analysis to reduce the high spectral dimension of HSI. Experiments results on Indian Pines (IP) and University of Pavia (UP) datasets proved the efficiency of the proposed approach. It increased the OA by 2.61% - 13% and the Kappa coefficient by 2.68% - 15:51% on IP dataset and increased the OA by 0.17% - 11% and the Kappa coefficient by 0.23% - 19% on UP dataset, which is superior to some state-of-art methods.</p>

scholarly journals Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

2021 ◽  
Vol 13 (3) ◽  
pp. 335
Author(s):  
Yuhao Qing ◽  
Wenyi Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Image Classification ◽  
Deep Network ◽  
Multi Scale

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

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