When using continuous frame space target images to perform space target detection, motion parameter estimation, and motion trajectory extraction, one problem that needs to be solved is the problem of entering and leaving the field of view of satellites and stars and other space targets. Detection is the basis of high-precision motion parameter estimation between frames. Use the STK EOIR module to generate space target sequence images. Based on the singular value decomposition theory, calculate the parameters such as the amount of rotation, translation, and scaling factors of the inter-frame image. The analysis of the inter-field parameters of the field of view identifies the points contained in and out of the field of view and detects them. The experimental results show that the algorithm is simple and fast, and can effectively detect space targets in and out of the field of view.
According to the latest satellite observations, clouds cover about 67% of the earth's surface and affect the radiation budget of the earth-atmosphere system by scattering and absorbing short-wave radiation from incident sunlight and longwave radiation from the ground. The characteristics of cloud particles at different altitudes are complex and changeable in space and time, the microphysical properties of real clouds are still unknown. As a kind of discrete random medium, cloud inevitably causes background interference to satellite-to-earth link laser communication, satellite remote sensing, etc. Therefore, the research on light scattering and radiation characteristics of clouds is not only helpful for predicting climate change and understanding the radiation budget of the global atmospheric system, but also has important significance for other fields in atmospheric physics. This paper starts with the modeling of the spatial distribution of the cloud, studies the spatial distribution patterns and internal microstructures of clouds, uses the multi-scale superposition algorithm in fractal theory to establish a three-dimensional spatial distribution model of clouds, and controls the parameters of the different types of clouds in the algorithm. The cloud layer can be regarded as an irregular structure composed of water droplets or ice crystal particles of various sizes in a three-dimensional space. The modeling of the spatial distribution of clouds includes two main parts: the modeling of clouds in spatial morphology and the modeling of the clouds in microscopic physical structure. For modeling of the spatial distribution of clouds, a multi-scale overlay algorithm is used. This algorithm has many controllable parameters and flexible control. It can generate various types of clouds according to needs, and can simulate dynamics by increasing the fractal dimension cloud structure. The multiscale superposition algorithm is used to establish the spatial distribution models of different types of clouds.
The spatial information of high-resolution remote sensing images is more abundant, and the expression of ground object information in detail is clearer. Vegetation is a component of the environment and the most important component of terrestrial ecosystems. Therefore, vegetation information extraction from remote sensing images is particularly significant. This paper takes Shanghai Pudong New Area as the research area, adopts threshold classification method and membership function classification method to extract vegetation information, and introduces normalized vegetation index as feature to extract vegetation information from WorldView-3 satellite remote sensing image. The results show that the accuracy of vegetation information extraction based on membership function classification method is higher. The classification accuracy of typical vegetation area is higher than 90%, and the Kappa coefficient is higher than 0.86, which can significantly reduce the fragmentation caused by classification. At the same time, high-resolution remote sensing images show great potential for the extraction of vegetation information in urban areas.
The light collected from remote sensors taken from space must transit through the Earth’s atmosphere. All satellite images are affected at some level by lightwave scattering and absorption from aerosols, water vapor and particulates in the atmosphere. For generating high-quality scientific data, atmospheric correction is required to remove atmospheric effects and to convert digital number (DN) values to surface reflectance (SR). Every optical satellite in orbit observes the earth through the same atmosphere, but each satellite image is impacted differently because atmospheric conditions are constantly changing. A physics-based detailed radiative transfer model 6SV requires a lot of key ancillary information about the atmospheric conditions at the acquisition time. This paper investigates to achieve the simultaneous acquisition of atmospheric radiation parameters based on the multi-spectral information, in order to improve the estimates of surface reflectance through physics-based atmospheric correction. Ancillary information on the aerosol optical depth (AOD) and total water vapor (TWV) derived from the multi-spectral information based on specific spectral properties was used for the 6SV model. The experimentation was carried out on images of Sentinel-2, which carries a Multispectral Instrument (MSI), recording in 13 spectral bands, covering a wide range of wavelengths from 440 up to 2200 nm. The results suggest that per-pixel atmospheric correction through 6SV model, integrating AOD and TWV derived from multispectral information, is better suited for accurate analysis of satellite images and quantitative remote sensing application.