scholarly journals DMF Data Format Usage for Change Detection

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Alejandro Cristo ◽  
David Valencia ◽  
Pablo J. Martínez ◽  
Rosa M. Pérez
Keyword(s):  
Remote Sensing ◽  
Data Structure ◽  
Data Fusion ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Great Accuracy ◽  
New Techniques ◽  
Data Format ◽  
Specific Data ◽  
The Earth

Because of the availability of an overwhelming amount of remote sensing data obtained by different instruments, new techniques and applications have been developed in order to pursue the objective of detecting changes that occur in a particular area of the Earth or that affect a large part of the Earth. These studies have used datasets covering different wavelength ranges (visible, IR, radar, and so on), but common to all of them is the necessity for great accuracy to ensure that no bias is introduced due to data correction. Otherwise, a result may be the generation of false positives. Also, many studies have used several different datasets for the same area to detect changes (this is usually called data fusion), but there exists no specific data structure designed for this purpose. In this paper, we propose a data structure to be used for accurate change detection. This structure is transparent to the user and can be used for data fusion to improve those studies.

scholarly journals Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection

2008 ◽  
Vol 46 (6) ◽  
pp. 1822-1835 ◽  
Author(s):  
G. Camps-Valls ◽  
L. Gomez-Chova ◽  
J. Munoz-Mari ◽  
J.L. Rojo-Alvarez ◽  
M. Martinez-Ramon
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Data Classification ◽  
Sensing Data

scholarly journals PERSPECTIVE OF USE OF REMOTE SENSING OF THE EARTH AND UNMANNED AERIAL VEHICLES IN THE AGRICULTURE OF TATARSTAN

2018 ◽  
Vol 12 (4) ◽  
pp. 17-19 ◽  
Author(s):  
Салават Сулейманов ◽  
Salavat Suleymanov ◽  
Николай Логинов ◽  
Nikolay Loginov
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicles ◽  
Remote Sensing Data ◽  
Soil Characteristics ◽  
Sensing Data ◽  
Natural Processes ◽  
Aerial Vehicles ◽  
The Earth ◽  
Constant Change ◽  
Vast Territory

The vast territory of Russia, occupied by agricultural lands, is difficult to control due to the lack of an undeveloped network of operational monitoring points, ground stations, including meteorological stations, lack of aviation support due to the high cost of maintaining staff, etc. In addition, due to various types of natural processes, there is a constant change in the boundaries of acreage, soil characteristics and vegetation conditions in different fields and from site to site. Abroad, the above mentioned problems are successfully solved due to the application of remote sensing data (RSD) of the Earth, obtained with the help of unmanned aerial vehicles (UAVs). The proceedings, obtained (UAV), can help both to solve complex tasks of managing agricultural territories, and in highly specialized areas.

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.

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