scholarly journals A SURVEY ON HYPERSPECTRAL IMAGE CLASSIFICATION USING ADAPTIVE SPATIAL-SPECTRAL FEATURE LEARNING

2019 ◽  
Vol 6 (9) ◽  
Author(s):  
G. ELAYAROJA ◽  
V. UMA SANKARI
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Feature Learning ◽  
Hyperspectral Image Classification

scholarly journals Adaptive Spatial-Spectral Feature Learning for Hyperspectral Image Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 61534-61547 ◽  
Author(s):  
Simin Li ◽  
Xueyu Zhu ◽  
Yang Liu ◽  
Jie Bao
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Feature Learning ◽  
Hyperspectral Image Classification

Graph-based spatial–spectral feature learning for hyperspectral image classification

2017 ◽  
Vol 11 (12) ◽  
pp. 1310-1316 ◽  
Author(s):  
Muhammad Ahmad ◽  
Adil Mehmood Khan ◽  
Rasheed Hussain
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Feature Learning ◽  
Hyperspectral Image Classification

scholarly journals Three-Dimensional ResNeXt Network Using Feature Fusion and Label Smoothing for Hyperspectral Image Classification

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1652 ◽  
Author(s):  
Peida Wu ◽  
Ziguan Cui ◽  
Zongliang Gan ◽  
Feng Liu
Keyword(s):  
Hyperspectral Image ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Spectral Feature ◽  
Feature Learning ◽  
Hyperspectral Image Classification ◽  
Training Samples ◽  
Dramatic Rise ◽  
Classification Tasks ◽  
The One

In recent years, deep learning methods have been widely used in the hyperspectral image (HSI) classification tasks. Among them, spectral-spatial combined methods based on the three-dimensional (3-D) convolution have shown good performance. However, because of the three-dimensional convolution, increasing network depth will result in a dramatic rise in the number of parameters. In addition, the previous methods do not make full use of spectral information. They mostly use the data after dimensionality reduction directly as the input of networks, which result in poor classification ability in some categories with small numbers of samples. To address the above two issues, in this paper, we designed an end-to-end 3D-ResNeXt network which adopts feature fusion and label smoothing strategy further. On the one hand, the residual connections and split-transform-merge strategy can alleviate the declining-accuracy phenomenon and decrease the number of parameters. We can adjust the hyperparameter cardinality instead of the network depth to extract more discriminative features of HSIs and improve the classification accuracy. On the other hand, in order to improve the classification accuracies of classes with small numbers of samples, we enrich the input of the 3D-ResNeXt spectral-spatial feature learning network by additional spectral feature learning, and finally use a loss function modified by label smoothing strategy to solve the imbalance of classes. The experimental results on three popular HSI datasets demonstrate the superiority of our proposed network and an effective improvement in the accuracies especially for the classes with small numbers of training samples.

Hyperspectral Image Classification Algorithm Based on Principal Component Texture Feature Deep Learning

2020 ◽  
Vol 10 (9) ◽  
pp. 2027-2031
Author(s):  
Xu Yifang
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Texture Feature ◽  
Feature Learning ◽  
Principal Component ◽  
Classification Algorithm ◽  
Classification Problems ◽  
Hyperspectral Image Classification ◽  
Reconstruction Performance

Hyperspectral image classification refers to a key difficulty on the domain of remote sensing image processing. Feature learning is the basis of hyperspectral image classification problems. In addition, how to jointly use the space spectrum information is Also an important issue in hyperspectral image classification. Recent ages have seen that as further exploration is developing, the method of hyperspectral image cauterization according to deep learning has been rapidly developed. However, existing deep networks often only consider reconstruction performance while ignoring the task itself. In addition, for improving preciseness of classification, most categorization methods use the fixed-size neighborhood of per hyperspectral pixel as the object of feature extraction, ignoring the identification and difference between the neighborhood pixel and the current pixel. On the basis of exploration above, our research group put forward with an image classification algorithm based on principal component texture feature deep learning, and achieved good results.

Multiple Feature Learning for Hyperspectral Image Classification

2015 ◽  
Vol 53 (3) ◽  
pp. 1592-1606 ◽  
Author(s):  
Jun Li ◽  
Xin Huang ◽  
Paolo Gamba ◽  
Jose M. Bioucas Bioucas-Dias ◽  
Liangpei Zhang ◽  
...  
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Feature Learning ◽  
Hyperspectral Image Classification ◽  
Multiple Feature
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