Accumulated snow identification method and system for synchronous satellite remote-sensing sequence images

Abstract

The invention discloses an accumulated snow identification method and a system for synchronous satellite remote-sensing sequence images. Specifically, the method comprises the steps of calculating a spectral angle of a to-be-identified multi-temporal image relative to a single-temporal accumulated snow sample, and calculating the luminance difference between the to-be-identified multi-temporal image and the single-temporal accumulated snow sample; based on the spectral angle and the luminance difference, subjecting the to-be-identified multi-temporal image to mask treatment and generating a multi-temporal high-brightness object image; calculating a dynamic time warping value, and subjecting the multi-temporal high-brightness object image to mask treatment based on the dynamic time warping value so as to generate an accumulated snow preliminary identification result image; based on the accumulated snow preliminary identification result image, obtaining an accumulated snow identification result image by conducting the classification result post-processing method. In this way, the multi-temporal characteristics of synchronous satellite remote-sensing sequence images are fully utilized, while the influence of clouds is eliminated without any short-wave infrared waveband. Therefore, the accumulated snow in synchronous satellite remote-sensing sequence images can be rapidly and accurately identified.

Description

technical field [0001] The invention relates to the field of classification and recognition of remote sensing images, in particular to a snow recognition method and system for synchronous satellite remote sensing sequence images. Background technique [0002] The spatial distribution of snow is an important input factor for hydrological and meteorological models in mountainous and seasonal snow-covered areas. The study of snow distribution and its characteristics is of great significance for atmospheric circulation analysis, climate evolution, water resource utilization and disaster analysis. [0003] Li Sanmei et al. studied the principle of snow cover identification of domestic stationary meteorological satellite FY-2C data, and proposed a snow cover identification method based on the auxiliary factor function of the threshold method, which better solved the difference in the vast area within the full disk of FY-2C. Real-time snow cover monitoring problem under underlying ...

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Problems solved by technology

However, these methods have many disadvantages: on the one hand, the preprocessing process is complicated, and different satellite data need to be geometrically corrected, atmospherically corrected, cropped, etc., and the accuracy of the geometric correction of the two data is difficult to ensure extremely high consistency

On the other hand, due to differences in spatial resolution, width, scanning area, etc., it is difficult to obtain two kinds of satellite data in the same area and at the same time, so the practicability of this method is low

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