LEO Satellite Constellation Images Real-time Haze Removal Technology

Dr. Yong Du

yongdu.ca@gmail.com

KSKY Hi-Tech Corp., Canada

Introduction:

Satellite constellation remote sensing has become increasingly important for environmental monitoring, disaster response, and natural resource management. However, haze and other atmospheric particles can significantly reduce the quality and usefulness of the data collected by satellite constellations. Real-time fully automatic haze removal technology is a promising solution that can improve the accuracy and usefulness of satellite constellation images.

Background:

Haze and other atmospheric particles can significantly reduce the quality and usefulness of satellite constellation images. Haze is caused by the scattering and absorption of sunlight by small particles and droplets in the atmosphere. This can make it difficult to detect and identify features on the Earth's surface, and can also distort the colors and brightness of the image.

Traditional methods of haze removal involve manually adjusting the image settings or using mathematical models to estimate the haze and remove it from the image. However, these methods are time-consuming and require significant expertise, making them unsuitable for real-time applications. Real-time fully automatic haze removal technology offers a more efficient and effective solution to this problem.

Real-time Fully Automatic Haze Removal Technology:

Real-time fully automatic haze removal technology uses machine learning algorithms to automatically detect and remove haze from satellite constellation images. This technology involves three main steps: image enhancement, feature extraction, and haze removal.

Image Enhancement:

The first step in real-time fully automatic haze removal is to enhance the satellite constellation image to improve its quality and make it easier to detect and identify features on the Earth's surface. This can involve adjusting the brightness and contrast of the image, as well as distinguishing noise and other distortions. Modern mathematics is applied into the features analysis and decomposition.  

Feature Extraction:

The second step is to extract features from the enhanced image that can be used to detect and remove haze. This can involve using machine learning algorithms to estimate parameters for identifying patterns in the image that are associated with haze, such as changes in brightness, color or multi-channel information.

Haze Removal:

The final step is to remove the haze from the image using a variety of techniques, such as dehazing algorithms or image fusion. These techniques can help to restore the colors and brightness of the image and improve the accuracy and usefulness of the data collected.

Advantages of Real-time Fully Automatic Haze Removal Technology:

Real-time fully automatic haze removal technology offers several advantages over traditional methods of haze removal. First, it is more efficient and cost-effective, as it can be performed in real-time without the need for manual intervention. This can help to reduce the time and cost of processing satellite constellation images. Second, real-time fully automatic haze removal technology is more accurate and reliable than traditional methods, as it uses machine learning algorithms to estimate parameters for automatically detecting and removing haze. This can help to improve the quality and usefulness of the data collected by satellite constellations. Finally, real-time fully automatic haze removal technology can support a wide range of applications, including environmental monitoring, disaster response, and natural resource management, etc. By providing more accurate and reliable data, such as detailed coordination of the target, this technology can help to improve decision-making and support more effective and efficient use of resources.

Conclusion:

Real-time fully automatic haze removal technology is a promising solution that can improve the accuracy and usefulness of satellite constellation images. This technology uses machine learning algorithms to estimate the parameters for automatically detecting and removing haze from satellite constellation images, and offers several advantages over traditional methods of haze removal. By providing more accurate and reliable data, real-time fully automatic haze removal technology can support a wide range of applications and help to improve decision-making in areas such as environmental monitoring, disaster response, and natural resource management, etc.

Running demo video available upon request!