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).

scholarly journals Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1734 ◽  
Author(s):  
Tien-Heng Hsieh ◽  
Jean-Fu Kiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Principal Components ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Agricultural Lands ◽  
Hyperspectral Image Classification ◽  
Spatial Features ◽  
Salinas Valley ◽  
Crop Agriculture

Several versions of convolutional neural network (CNN) were developed to classify hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral data, 1D-CNN with selected bands, 1D-CNN with spectral-spatial features and 2D-CNN with principal components. The HSI data of a crop agriculture in Salinas Valley and a mixed vegetation agriculture in Indian Pines were used to compare the performance of these CNN algorithms. The highest overall accuracy on these two cases are 99.8% and 98.1%, respectively, achieved by applying 1D-CNN with augmented input vectors, which contain both spectral and spatial features embedded in the HSI data.

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