Optimal selection from extremely redundant satellite images for efficient large-scale mapping

Abstract

Nowadays, the substantially increasing optical remote sensing satellites are constantly generating tremendous amount of images. However, superabundant images would easily lead to unnecessary computation and time costs for photogrammetric mapping product generation; thus, data redundancy should be properly reduced to improve production efficiency. In this study, we proposed an optimal selection method for extracting a minimal subset from extremely redundant satellite images, aiming at providing full coverage of the area of interest (AOI) while maintaining the minimally required overlap between adjacent scenes for efficient large-scale mapping applications. We first constructed a novel optimization model by rasterizing the target AOI into regular grids and converting the image selection problem into a grid voting problem. Then, we applied the constraints on image quality, which we efficiently quantified using metadata information, and the distribution reasonability, which we designed by penalizing the adjacent grids voting for different images, to the model to achieve an optimal selection result. We modeled the optimization problem as a Markov random field. The experimental results on four datasets, which all cover large-scale areas with 165,456, 38,252, 729, and 25,922 scenes of optical satellite images, respectively, demonstrated that the proposed approach can substantially reduce the amount of raw data whiling maintaining high image quality and sufficient overlap. The quantitative evaluation indicated that the proposed method considerably outperformed the state-of-the-art methods in most of eight evaluation metrics describing data quality, simplicity, and feasibility. Furthermore, the additional mapping production experiments on the provincial-level dataset, revealed that the proposed image selection method can significantly improve the production efficiency with only a marginal decrease in product accuracy.