Hyperspectral Data Classification to Support the Radiometric Correction of Thermal Imagery

2017 ◽  
pp. 81-92 ◽  
Author(s):  
Gabriele Bitelli ◽  
Rita Blanos ◽  
Paolo Conte ◽  
Emanuele Mandanici ◽  
Paolo Paganini ◽  
...  
Keyword(s):  
Data Classification ◽  
Hyperspectral Data ◽  
Thermal Imagery ◽  
Radiometric Correction

Regularized RBF Networks for Hyperspectral Data Classification

2004 ◽  
pp. 429-436 ◽  
Author(s):  
G. Camps-Valls ◽  
A. J. Serrano-López ◽  
L. Gómez-Chova ◽  
J. D. Martín-Guerrero ◽  
J. Calpe-Maravilla ◽  
...  
Keyword(s):  
Data Classification ◽  
Hyperspectral Data ◽  
Rbf Networks

Nonlinear Correlation Coefficient Based Kernel Method for Hyperspectral Data Classification

2009 ◽  
Vol 29 (9) ◽  
pp. 2607-2614
Author(s):  
张淼 Zhang Miao ◽  
沈毅 Shen Yi ◽  
王强 Wang Qiang
Keyword(s):  
Correlation Coefficient ◽  
Kernel Method ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Nonlinear Correlation

scholarly journals Quasiconformal Mapping Kernel Machine Learning-Based Intelligent Hyperspectral Data Classification for Internet Information Retrieval

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jing Liu ◽  
Yulong Qiao
Keyword(s):  
Machine Learning ◽  
Quasiconformal Mapping ◽  
Kernel Function ◽  
Hyperspectral Image ◽  
Data Classification ◽  
Data Retrieval ◽  
Hyperspectral Data ◽  
The Internet ◽  
Discriminative Ability ◽  
Kernel Machine

Intelligent internet data mining is an important application of AIoT (Artificial Intelligence of Things), and it is necessary to construct large training samples with the data from the internet, including images, videos, and other information. Among them, a hyperspectral database is also necessary for image processing and machine learning. The internet environment provides abundant hyperspectral data resources, but the hyperspectral data have no class labels and no so high value for applications. So, it is important to label the class information for these hyperspectral data through machine learning-based classification. In this paper, we present a quasiconformal mapping kernel machine learning-based intelligent hyperspectral data classification algorithm for internet-based hyperspectral data retrieval. The contributions include three points: the quasiconformal mapping-based multiple kernel learning network framework is proposed for hyperspectral data classification, the Mahalanobis distance kernel function is as the network nodes with the higher discriminative ability than Euclidean distance-based kernel function learning, and the objective function of measuring the class discriminative ability is proposed to seek the optimal parameters of the quasiconformal mapping projection. Experiments show that the proposed scheme is effective for hyperspectral image classification and retrieval.

scholarly journals Deep Learning for Hyperspectral Data Classification through Exponential Momentum Deep Convolution Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qi Yue ◽  
Caiwen Ma
Keyword(s):  
Data Classification ◽  
Classification Problem ◽  
Hyperspectral Data ◽  
Support Vector ◽  
New Classification ◽  
Spatial Features ◽  
Classification Framework ◽  
Deep Convolution Neural Network ◽  
Quantitative Validation ◽  
High Level

Classification is a hot topic in hyperspectral remote sensing community. In the last decades, numerous efforts have been concentrated on the classification problem. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on complex handcrafted features. However, it is rarely known which features are important for the problem. In this paper, a new classification skeleton based on deep machine learning is proposed for hyperspectral data. The proposed classification framework, which is composed of exponential momentum deep convolution neural network and support vector machine (SVM), can hierarchically construct high-level spectral-spatial features in an automated way. Experimental results and quantitative validation on widely used datasets showcase the potential of the developed approach for accurate hyperspectral data classification.

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