Algorithm For Preprocessing Satellite Imagery That Uses Geometric, Atmospheric, And Radiometric Correction

In the last few years, the topic of remote sensing image processing has gained a lot of interest. Object identification, picture categorization, and image retrieval are all included in satellite image processing. Because satellite data can take detailed, large-scale images of the Earth's surface over time, it can be a valuable tool for understanding and managing both natural and artificial ecosystems. Due to a variety of circumstances, such as atmospheric disruption, absorption, scene illumination, satellite orbit motion, and geometrical distribution, raw images obtained from various satellite sensors frequently exhibit distortion and inconsistency. To ensure proper analysis, satellite images must be preprocessed effectively to remove distortions and improve image quality. In order to preprocess raw landslide satellite photos, this study presents a deep learning framework that combines advanced deep learning techniques with more traditional methods including noise reduction, geometry correction, atmospheric correction, radiometric correction, and image enhancement. Preprocessing improves image clarity and inconsistency, which increases dataset accuracy. This work also provides a quick overview of several satellite picture defects and demonstrates how preprocessing greatly enhances landslide detection analysis. This method provides a significant advancement in the preprocessing of satellite data, enabling more intelligent geographic decision-making.