Hyperspectral remote sensing image classification based on dense residual three-dimensional convolutional neural network

2020 ◽  
Author(s):  
Suting Chen ◽  
Meng Jin ◽  
Jie Ding
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Three Dimensional ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Image Classification ◽  
Hyperspectral Remote Sensing Image

A non-local capsule neural network for hyperspectral remote sensing image classification

2021 ◽  
Vol 12 (1) ◽  
pp. 77-86
Author(s):  
Runmin Lei ◽  
Chunju Zhang ◽  
Shihong Du ◽  
Chen Wang ◽  
Xueying Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Image Classification ◽  
Non Local ◽  
Hyperspectral Remote Sensing Image

scholarly journals An Improved Neural Network Algorithm for Remote Sensing Image Classification

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Classification ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Processing Efficiency ◽  
Remote Sensing Image Classification ◽  
Hyperspectral Remote Sensing Image

The appearance of hyperspectral remote sensing image further improves the accuracy of remote sensing image classification, but the data of hyperspectral remote sensing image is large, and the processing hyperspectral remote sensing image has high complexity and low efficiency. A remote sensing image classification algorithm based on improved bilinear recurrent neural network (BLRNN) model is proposed, this paper gives the definition of bilinear recurrent neural Network and the description of network structure, and optimizes and improves the bilinear recurrent neural network from two aspects of network structure and pruning process, and uses the genetic algorithm global search to trim. Compared with the original feature, PCA and BPNN algorithm, the results show that the BLRNN algorithm has been greatly improved in classification accuracy and classification time, and the image processing efficiency is improved.

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