Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multitemporal and multisatellite studies or comparisons beten satellite data and local ground measurements require nowadays precise and automatic geometric correction of satellite images.  presents a fully automatic geometric correction system capable of georeferencing satellite images with high accuracy. An orbital prediction model, which provides initial earth locations, is combined with the proposed automatic contour-matching technique. This combination allows correcting the low-frequency error component, mainly due to timing and orbital model errors, as ll as the high-frequency error component, due to variations in the spacecraft's attitude. The approach aims at exploiting the maximum reliable information in the image to guide the matching algorithm. The contour-matching process has three main steps: 1) estimation of the gradient energy map (edges) and detection of the cloudless (reliable) areas; 2) initialization of the contours positions; 3) estimation of the transformation parameters (affine model) using a contour optimization approach. Three different robust and automatic algorithms are proposed for optimization, and their main features are discussed. Finally, the performance of the three proposed algorithms is assessed using a new error estimation technique applied to Advanced Very High Resolution Radiometer (AVHRR), Sea-viewing Wide Field of view Sensor (SeaWiFS), and multisensor AVHRR-SeaWiFS imagery.
['Francisco Eugenio', 'Ferran Marques']
I am writing a research paper on a idea Automatic satellite image georeferencing using a contour-matching approach
[{"from": "human", "value": "I am writing a research paper on a idea Automatic satellite image georeferencing using a contour-matching approach"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multitemporal and multisatellite studies or comparisons beten satellite data and local ground measurements require nowadays precise and automatic geometric correction of satellite images.  presents a fully automatic geometric correction system capable of georeferencing satellite images with high accuracy. An orbital prediction model, which provides initial earth locations, is combined with the proposed automatic contour-matching technique. This combination allows correcting the low-frequency error component, mainly due to timing and orbital model errors, as ll as the high-frequency error component, due to variations in the spacecraft's attitude. The approach aims at exploiting the maximum reliable information in the image to guide the matching algorithm. The contour-matching process has three main steps: 1) estimation of the gradient energy map (edges) and detection of the cloudless (reliable) areas; 2) initialization of the contours positions; 3) estimation of the transformation parameters (affine model) using a contour optimization approach. Three different robust and automatic algorithms are proposed for optimization, and their main features are discussed. Finally, the performance of the three proposed algorithms is assessed using a new error estimation technique applied to Advanced Very High Resolution Radiometer (AVHRR), Sea-viewing Wide Field of view Sensor (SeaWiFS), and multisensor AVHRR-SeaWiFS imagery."}]