scholarly journals Automatic Scheduling Tool for Balloon-Borne Planetary Optical Remote Sensing

2021 ◽  
Vol 13 (7) ◽  
pp. 1291
Author(s):  
Zhen Shi ◽  
Yong Zhao ◽  
Fei He ◽  
Zhonghua Yao ◽  
Zhaojin Rong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Solar System ◽  
Reference Frames ◽  
Planetary Atmosphere ◽  
Space Environment ◽  
Maximum Efficiency ◽  
Optical Remote Sensing ◽  
Imaging Data ◽  
Scheduling Tool ◽  
Automatic Scheduling

The balloon-borne Planetary Atmosphere Spectroscopic Telescope (PAST), China’s first planetary optical remote-sensing project, will be launched for testing and conducting scientific flights during 2021 and 2022. Images of the planetary atmosphere and plasma in ultraviolet and visible wavelengths will be used to investigate the diversity of the planetary space environment in the solar system and their different drivers. Because simultaneous observation of multiple target planets in the solar system is possible, effective observation scheduling is critical to acquire high scientific merit spectroscopic imaging data. Herein, we demonstrate an automatic scheduling tool (AST) to aid the planning of observation schedules. The AST is primarily based on a planetary ephemeris and is realized on the basis of the geometrical information and optical requirements of the telescope. The temporal variations of the planetary reference frames can also be obtained to assist in the positioning and data processing of the telescope. As a part of the Chinese deep-space exploration plan, several ground-based planetary optical telescopes will be constructed in China in the future. With the use of the proposed AST, such telescopes can achieve maximum efficiency.

scholarly journals OPTICAL REMOTE SENSING IMAGE OPTIMIZED DEHAZING ALGORITHM BASED ON HOT

2016 ◽  
Vol XLI-B3 ◽  
pp. 797-803
Author(s):  
Zhou Yang ◽  
Xu Qing ◽  
Xu Jiwei ◽  
Jin Guowang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Satellite Images ◽  
Space Environment ◽  
Optical Remote Sensing ◽  
Near Earth Space ◽  
Texture Information ◽  
Optical Remote Sensing Image ◽  
Dark Pixel ◽  
Application Fields

Due to the significantly effect of clouds in the near-earth space environment to remote sensing satellite images, some satellite images can not be utilized normally, resulting in large limitation of their application fields. For the background suppressed haze thickness index (BSHTI) and improvement background suppressed haze thickness index (IBSHTI) have the characteristics of thoroughly cloud correction and badly deficiency of the tone and texture information, we propose to first adopt IBSHTI to calculate the cloud thickness image of different bands, then the dark-pixel images are obtained by down sampling, and the texture is eliminated by introducing Texture and edge information (TEI). Experiment results show that our method can well retain the ground tone and texture information while removing the effect of clouds, especially in urban areas.

scholarly journals OPTICAL REMOTE SENSING IMAGE OPTIMIZED DEHAZING ALGORITHM BASED ON HOT

2016 ◽  
Vol XLI-B3 ◽  
pp. 797-803
Author(s):  
Zhou Yang ◽  
Xu Qing ◽  
Xu Jiwei ◽  
Jin Guowang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Satellite Images ◽  
Space Environment ◽  
Optical Remote Sensing ◽  
Near Earth Space ◽  
Texture Information ◽  
Optical Remote Sensing Image ◽  
Dark Pixel ◽  
Application Fields

Due to the significantly effect of clouds in the near-earth space environment to remote sensing satellite images, some satellite images can not be utilized normally, resulting in large limitation of their application fields. For the background suppressed haze thickness index (BSHTI) and improvement background suppressed haze thickness index (IBSHTI) have the characteristics of thoroughly cloud correction and badly deficiency of the tone and texture information, we propose to first adopt IBSHTI to calculate the cloud thickness image of different bands, then the dark-pixel images are obtained by down sampling, and the texture is eliminated by introducing Texture and edge information (TEI). Experiment results show that our method can well retain the ground tone and texture information while removing the effect of clouds, especially in urban areas.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Design of global-contextual detection model for optical remote sensing targets

2020 ◽  
Vol 13 (6) ◽  
pp. 1-12
Author(s):  
ZHANG Rui-yan ◽  
◽  
JIANG Xiu-jie ◽  
AN Jun-she ◽  
CUI Tian-shu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing ◽  
Detection Model

Inverse Problems in Optical Remote Sensing of Coastal Waters

2006 ◽  
Author(s):  
Irina Dolina ◽  
Lev Dolin ◽  
Alexander Luchinin ◽  
Iosif Levin ◽  
Liza Levina
Keyword(s):  
Remote Sensing ◽  
Inverse Problems ◽  
Coastal Waters ◽  
Optical Remote Sensing
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