High-resolution remote sensing image impervious surface extraction method based on cross-sensor migration

Abstract

The invention relates to a high-resolution remote sensing image impervious surface extraction method based on cross-sensor migration. According to the invention, a deep learning theory is combined, a cross-sensor normalization layer is designed, self-radiation characteristic normalization parameters of the sensors are hierarchically and automatically learned in a data driving mode, and characteristic differences among different remote sensing sensors are eliminated; sensor related adversarial optimization training is designed, ground object semantic related convolution parameters and sensor related normalization parameters are efficiently optimized, and the migration capability of a deep learning model between different data source images is enhanced. According to the invention, the problem that cross-sensor migration is difficult due to the fact that an existing deep migration learning method cannot consider sensor characteristic differences can be solved, migration of a deep impervious surface extraction model from a high-resolution airborne image to a satellite-borne image is achieved, and the precision of cross-sensor migration classification and mapping is effectively improved.

Description

technical field [0001] The invention is based on the technical processing field of remote sensing images, and in particular relates to a method for extracting impermeable surfaces of high-resolution remote sensing images based on cross-sensor migration. Background technique [0002] With the rapid development of earth observation technology, massive multi-source high-resolution remote sensing images are continuously acquired by various sensors. High-resolution imagery makes smaller ground objects visible and can be used to explore fine structures within cities. Urban impervious surfaces refer to the types of man-made surface coverings, such as roads, buildings, and parking lots, that prevent water from seeping into the soil. Efficient and accurate mapping and monitoring of urban impervious surfaces based on multi-source high-resolution remote sensing images can provide important information for urban planning and disaster prevention in the process of urbanization. [0003]...

AI Technical Summary

Problems solved by technology

However, the existing deep learning methods have not considered the characteristics of different remote sensing sensors, resulting in poor method transferability between different sensors

Network models trained on small-scale aerospace image impermeable surface datasets are often not suitable for large-scale aerial image classification and mapping

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