Cloud atlas segmentation method based on FCN and CNN

Abstract

The invention provides a cloud atlas segmentation method based on a FCN and a CNN, and belongs to the field of image segmentation of computer vision. The method is characterized in that firstly, a near neighborhood of each pixel point in a cloud atlas is correspondingly clustered through an ultra-pixel, the cloud atlas is input to fully convolutional networks FCN32s and FCN8s with different step lengths, and pre-segmentation results of the cloud atlas are realized; in a FCN32s result map, a black area is a part of a non-cloud area in the cloud atlas, and in a FCN8s result map, a white area is a part of a cloud area in the cloud atlas; and remaining uncertain areas are grey areas which need to be determined through a deep convolutional neural network (CNN), key pixels in an ultra-pixel area need to be selected to represent characterizes of the ultra-pixel area, and whether the characteristics of the pixels are cloud or non-cloud is determined through the CNN network. According to the method, the precision is equivalent to that of MR-CNN and SP-CNN, and the speed is increased by 880 times compared with MR-CNN and increased by 1.657 times compared with SP-CNN.

Description

technical field [0001] The invention belongs to the field of image segmentation of computer vision, relates to superpixel clustering and feature extraction of various neural networks, and specifically relates to superpixel preprocessing of millimeter-wave radar cloud images and through full convolutional neural network FCN and convolutional neural network CNN Feature extraction, a FCN_CNN based millimeter wave radar cloud image segmentation method is proposed. Background technique [0002] Image segmentation is one of the key technologies in image processing. Image segmentation has attracted people's attention since the 1970s, and its application fields have been very extensive since its development. It is mainly manifested in: the field of military research, such as realizing military target positioning and battlefield analysis through image segmentation; the field of medical imaging, such as assisting in the analysis of organs and diseases through image segmentation; the ...

AI Technical Summary

Problems solved by technology

However, the distribution of "cloud" or "non-cloud" in the millimeter-wave cloud image used in the present invention is not necessarily continuous, and it is still impossible to judge whether it is a cloud or a non-cloud after the pixels in the neighborhood are clustered.

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