Abstract
Determining appropriate master images, reducing radiometric error accumulation, and eliminating outliers from the cloud, water, and land changes, are three main issues in radiometric normalization of multitemporal high-resolution satellite images (HRSI) during mosaicking. However, these three issues have not been simultaneously considered by the existing methods. This article presents a comprehensive radiometric normalization method for multitemporal HRSI using a radiometric block adjustment without master images. Pseudoinvariant features (PIFs) extracted from image pairs using the iteratively reweighted multivariate alteration detection are used as the corresponding pixel observations and organized to form radiometric tie points according to the corresponding horizontal space coordinates. Radiometric error equations are subsequently constructed, and the linear radiometric transformation parameters are solved by a global adjustment. The time-invariant PIFs generally represent the true corresponding features and naturally avoid the cloud, water, and land changes, which can eliminate the effects of outliers. Furthermore, the pixel values of tie points calculated from the weighted average of the corresponding pixel observations are used as virtual radiometric control points to eliminate the dependency on master images. Moreover, a global optimum can be achieved by the global adjustment, effectively overcoming the error accumulation, which is severe in large datasets. Four groups of HRSI datasets from various satellites are used to validate the performance of the proposed method. Experimental results demonstrate that the proposed method outperforms two state-of-the-art methods and has good applicability and stability, considering both visual effects and quantitative performance.
Highlights
R ADIOMETRIC normalization aims at reducing the radiation differences between images by adjusting the color of each image [1]
Relative radiometric normalization methods are widely used for the color balancing of multitemporal high-resolution satellite images (HRSI) mosaic in various cases
The virtual radiometric control point (VRCP), which is the weighted average of the corrected radiation observations of the radiometric tie points (RTPs), is introduced as the true value corresponding to the observation in radiometric block adjustment, to reduce the dependence of the proposed method on the master images
Summary
R ADIOMETRIC normalization aims at reducing the radiation differences between images by adjusting the color of each image [1]. Absolute radiometric normalization methods aim to obtain surface reflectance by eliminating the errors in radiation transmission with a series of treatments, such as radiometric calibration and atmospheric correction [2], [3] This type of method requires external information, namely, simultaneous meteorological data and ground-measured reflectance of objects. The article presents a comprehensive relative radiometric normalization method for HRSI via radiometric block adjustment without selecting master images to solve three problems simultaneously. The virtual radiometric control point (VRCP), which is the weighted average of the corrected radiation observations of the RTP, is introduced as the true value corresponding to the observation in radiometric block adjustment, to reduce the dependence of the proposed method on the master images.
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