A new framework for hyperspectral image classification using Gabor embedded patch based convolution neural network

2020 ◽  
Vol 110 ◽  
pp. 103455 ◽  
Author(s):  
L.N. Phaneendra Kumar Boggavarapu ◽  
Prabukumar Manoharan
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Convolution Neural Network ◽  
Hyperspectral Image Classification ◽  
New Framework

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).

Lightweight Tensor Attention-Driven ConvLSTM Neural Network for Hyperspectral Image Classification

2021 ◽  
Vol 15 (3) ◽  
pp. 734-745
Author(s):  
Wen-Shuai Hu ◽  
Heng-Chao Li ◽  
Yang-Jun Deng ◽  
Xian Sun ◽  
Qian Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification
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