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Satellite remote sensing technology is a comprehensive technical system for observing the Earth and celestial bodies from platforms on the ground to space. Satellite-based remote sensing can acquire satellite data from remote sensing technology platforms and involves remote sensing instruments and the reception, processing, and analysis of information. Remote sensing technology is an advanced and rapidly developing technology that forms an information network through satellite-based remote sensing, continuously providing people with large amounts of scientific data and dynamic information.
To eliminate various radiation and geometric distortions, allowing the processed images to more accurately represent the true appearance of the original scenes.
To use enhancement technology to highlight certain spectral and spatial features of the scenes, making it easier to interpret and distinguish them from other ground features.
To further understand, analyze, and interpret the processed images, extracting the required thematic information. Remote sensing information processing is divided into two categories: analog processing and digital processing.
Conventional satellite remote sensing data processing tasks include correction and calibration, stitching and mosaicking, color adjustment, matching and fusion, image overlay, data partitioning, vector correction, coordinate transformation, classification extraction, orthorectification, vectorization, 3D modeling, and post-production mapping.
Correction and Calibration
Adjacent images may experience shifts, stretching, and distortion due to factors such as imaging dates, platform posture, altitude, and speed. Correction and calibration of these geometric distortions address issues like road displacement and building fault lines.
Stitching/Mosaicking
Data captured by satellites is typically in strip formats. When the required area spans multiple strips, it is necessary to stitch and calibrate the data delivered by satellite companies to ensure the final result appears seamless, as if derived from a single dataset. Inferior results will visibly show road displacement and building fault lines.
Color Adjustment
Due to differences in imaging dates and system processing conditions, there may be radiometric level differences that cause inconsistencies in the brightness values of identical ground features in adjacent images. If color adjustment is not performed before mosaicking such images, even if geometric registration is accurate and the overlap areas match well, there will be noticeable color discrepancies along the seams, making the images unsightly and impacting the analysis and identification of ground features and professional information, reducing application effectiveness.
By adjusting the parameters for banded data, the overall color tone after mosaicking shows minimal differences and looks visually appealing. However, due to differences in temporal and weather conditions, there may be some color deviations that can be adjusted as needed to ultimately obtain color-accurate image data.
Matching and Fusion
We can match and fuse panchromatic data and multispectral data from different satellites to obtain high-resolution true-color satellite image data products post-fusion.
The development of satellite remote sensing technology provides abundant multisource remote sensing data. This data from different sensors has varying temporal, spatial, and spectral resolutions as well as different polarizable modes. Single sensor data often cannot meet application needs, so image fusion can extract more useful information from various satellite remote sensing images, supplementing the deficiencies of single sensors.
Image fusion refers to the process of combining multisource satellite remote sensing images using specific algorithms within a specified geographic coordinate system to generate new images. Panchromatic images typically have higher spatial resolution (e.g., ALOS panchromatic images with a resolution of 2.5m), while multispectral images have richer spectral information (e.g., ALOS with three bands). To improve the spatial resolution of ALOS multispectral images, we can fuse panchromatic images with multispectral images, enhancing spatial resolution while retaining multispectral characteristics.
By selecting the optimal method from various resolution fusion techniques, we can merge panchromatic and multispectral bands to achieve images with high spatial resolution and texture characteristics, as well as rich spectral information, resulting in richly informative and visually appealing high-quality image maps.
Cloud Removal
During actual satellite-based remote sensing imaging, the presence of cumulus clouds, mountainous clouds, and regional fog often results in blurry images, reducing image clarity and resolution. Post-processing technology can remove clouds and fog to achieve optimal image effects.
Stereo Pair Extraction of DEM
A Digital Elevation Model (DEM) is a representation of ground elevations using an array of ordered numerical values. Besides ground elevation information, DEM can also derive terrain characteristics such as slope and aspect and calculate terrain feature parameters, including peaks, ridges, plains, planes, channels, and valleys.
Kaiyun Joint Satellite Remote Sensing Data Processing Platform
The system adopts digital photogrammetry, multitask scheduling technology, and high-performance computing technology, integrating data production, task management scheduling, and result quality inspection into a single system. Through efficient allocation of computing resources and data resources, it realizes scalable, fast, and intelligent remote sensing data processing. The system is capable of receiving and processing high-resolution satellite remote sensing images and quickly generating various data processing products and remote sensing interpretation products.
The system produces vast, full-time-space coverage (massive), multi-satellite collaborative processing (hybrid), and continuously updated (rapid) remote sensing big data products.
It supports various parallel computing methods and provides efficient image processing algorithms.
The system supports processing of multisource, multi-payload satellite remote sensing images, including optical, radar, and hyperspectral data.
It supports numerous satellite images, including GF1\2\3\4\5\6, BJ1\2, ZY3, SV, TH, 02C, WV, QB, and IKNOS.