Foundation automatic cloud detection method based on superpixel division

Abstract

The invention discloses a foundation automatic cloud detection method based on superpixel division. The foundation automatic cloud detection method based on the superpixel division comprises the steps of dividing an input red-green-blue (GRB) cloud picture into a plurality of superpixel blocks with a superpixel division algorithm, obtaining a characteristic picture according to a picture with various color channels, calculating a local threshold value of each superpixel block, obtaining a threshold value matrix by using bilinear interpolation algorithm, obtaining an judging result of each pixel point by comparing the threshold value matrix and the characteristic picture, and obtaining an cloud detection result of the input GRB cloud picture by using the judging result of each pixel point and correspondence of locations of the pixel points. According to the foundation automatic cloud detection method based on the superpixel division, consistency of the superpixel blocks is maintained to the maximum extend, a good cloud detection result is obtained, and the robustness and the accuracy are high.

Description

Technical field [0001] The invention belongs to the technical field of image processing, in particular to a ground-based cloud automatic detection method based on super pixel segmentation. Background technique [0002] Cloud is the external manifestation of thermal and dynamic processes in the atmosphere. Its generation and evolution are one of the specific manifestations of the intricate physical processes that occur in the atmosphere. It not only reflects the atmospheric movement, stability, and water vapor at the time, but also predicts the future. The weather trend within a certain period of time. Therefore, cloud observation is an important part of meteorological observation. Accurately acquiring cloud information is of great significance to many fields such as weather forecast, national economy and military support. At present, cloud observation is mainly done through ground-based observation and satellite remote sensing. Among them, satellite remote sensing has achieved ...

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Problems solved by technology

Long et al. proposed to use a fixed threshold for cloud detection. Obviously, the fixed threshold cannot be applied to all cloud images. Yang Jun et al. proposed the method of applying the maximum inter-class variance threshold method to adaptively calculate the threshold for different cloud images. However, due to the cloud shape Due to the ever-changing and the influence of illumination, using a global threshold for the entire cloud image cannot obtain good detection accuracy

Later, Yang Jun et al. proposed a local threshold interpolation method based on Ng's valley threshold selection method. This method is to first divide the image into image blocks of the same size, and then calculate the threshold for each image block. However, due to the size of the cloud block in the cloud image , shape, and position are complex and changeable, and such a rigid division of the cloud image obviously cannot obtain good detection results

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