Multiple Multi-Spectral Remote Sensing Data Fusion and Integration for Geological Mapping

2019 ◽  
Author(s):  
Mahendra K. Pal ◽  
Thorkild M. Rasmussen ◽  
Mehdi Abdolmaleki
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Geological Mapping ◽  
Sensing Data

scholarly journals A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

2021 ◽  
Author(s):  
Peng Liu
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Observation Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Adversarial Networks ◽  
Active Research

In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

Information Extraction and Metallogenic Prediction of Qiangduo Area in Tibet Based on Multi-Source Remote Sensing Data

2014 ◽  
Vol 962-965 ◽  
pp. 127-131
Author(s):  
Xin Xing Liu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Geological Mapping ◽  
Advanced Technology ◽  
Data Types ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Metallogenic Prediction

Remote sensing technology as a kind of new and advanced technology has been playing an important role in geological mapping and prospecting. A single kind of remote sensing data always has both advantages and disadvantages. And with multispectral remote sensing data types increasing, the integrated application of multi-source remote sensing data will be one of the development trend of remote sensing geology. In this paper, comprehensive utilization of multi-source remote sensing data such as ETM+, ASTER, Worldview-II and DEM, lithology and geological structure of Qiangduo area in Tibet were interpreted in different levels and mineralized alteration information also was extracted. Then on the basis of modern metallogenic theory, analyzed the multiple mineralization favorite information, established the remote sensing prediction model, and on the GIS platform, carried out metallogenic prediction of the study area. The field validation shows that the results of the prediction are relatively accurate and remote sensing technology can improve the efficiency of geological work.

Remote sensing data fusion and machine learning techniques for mineral exploration

2020 ◽  
Author(s):  
Priscilla Addison ◽  
Stephen Alwon ◽  
Alex Janevski ◽  
Kristopher Purens ◽  
Clyde Wheeler
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Data Fusion ◽  
Mineral Exploration ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Sensing Data ◽  
Learning Techniques
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