Study on spacecraft motion compensation technology for Chinese geostationary meteorological satellite remote sensing image

2007 ◽  
Author(s):  
Yankai Cui ◽  
Zhigang Wang
Keyword(s):  
Remote Sensing ◽  
Motion Compensation ◽  
Satellite Remote Sensing ◽  
Remote Sensing Image ◽  
Spacecraft Motion ◽  
Geostationary Meteorological Satellite ◽  
Meteorological Satellite

scholarly journals A MACHINE LEARNING DATASET FOR LARGE-SCOPE HIGH RESOLUTION REMOTE SENSING IMAGE INTERPRETATION CONSIDERING LANDSCAPE SPATIAL HETEROGENEITY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 731-736
Author(s):  
Y. Xu ◽  
X. Hu ◽  
Y. Wei ◽  
Y. Yang ◽  
D. Wang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Spatial Heterogeneity ◽  
Satellite Remote Sensing ◽  
Sampling Method ◽  
National Level ◽  
Remote Sensing Image ◽  
Training Sample ◽  
Large Scope

<p><strong>Abstract.</strong> The demand for timely information about earth’s surface such as land cover and land use (LC/LU), is consistently increasing. Machine learning method shows its advantage on collecting such information from remotely sensed images while requiring sufficient training sample. For satellite remote sensing image, however, sample datasets covering large scope are still limited. Most existing sample datasets for satellite remote sensing image built based on a few frames of image located on a local area. For large scope (national level) view, choosing a sufficient unbiased sampling method is crucial for constructing balanced training sample dataset. Dependable spatial sample locations considering spatial heterogeneity of land cover are needed for choosing sample images. This paper introduces an ongoing work on establishing a national scope sample dataset for high spatial-resolution satellite remote sensing image processing. Sample sites been chosen sufficiently using spatial sampling method, and divided sample patches been grouped using clustering method for further uses. The neural network model for road detection trained our dataset subset shows an increased performance on both completeness and accuracy, comparing to two widely used public dataset.</p>

scholarly journals A Study on Brightness Reversal of Internal Waves in the Celebes Sea Using Himawari-8 Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3831
Author(s):  
Beilei Hu ◽  
Junmin Meng ◽  
Lina Sun ◽  
Hao Zhang
Keyword(s):  
Remote Sensing ◽  
Internal Waves ◽  
Zenith Angle ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Angle Sensor ◽  
Reversal Phenomenon ◽  
Geostationary Meteorological Satellite ◽  
First Time ◽  
Meteorological Satellite

A geostationary meteorological satellite is located at a fixed point above the equator, which can continuously observe internal waves and provides great advantages in research on changes in the generation and propagation of internal waves. The scale of internal waves in the Celebes Sea is large, which is still very obvious in geostationary meteorological satellite images with a lower spatial resolution. This study considers continuous remote sensing images of geostationary meteorological satellite Himawari-8 to analyze the bright and dark features of internal waves in the Celebes Sea in optical remote sensing images. The solar zenith angle, sensor zenith angle and relative azimuth angle of internal waves in six images are calculated, and the changes are 12.45°, 0.20° and 3.44°, respectively, within 50 min. Moreover, based on the normalized sunglint radiance theory, the critical solar viewing angle is proposed and verified. The results indicate that the bright and dark features of internal waves when passing through sunglint and non-sunglint areas are greatly reversed, and the critical solar viewing angles are 18.73° and 27.41°, respectively. In this study, geostationary meteorological satellite Himawari-8 images are analyzed to study on the brightness reversal phenomenon of internal waves for the first time, and a unique brightness change in internal waves during the propagation process is revealed, which has not been reported in existing research.

Sign in / Sign up

Export Citation Format

Share Document