Support vector machine-based cloud, snow and fog detection method for optical satellite remote sensing image

Abstract

The invention discloses a support vector machine-based cloud, snow and fog detection method for an optical satellite remote sensing image. The method comprises the following steps of firstly, collecting different types of a large amount of ground object and cloud, snow and fog sample image data to serve as a training set, obtaining grayscale and texture features of images to form a feature set, and performing machine learning on the feature set of all samples through a support vector machine method to obtain cloud, snow and fog image classifiers; secondly, determining the types of the to-be-detected images by using the obtained cloud, snow and fog image classifiers, performing morphological close operation and overlapping region correction, and judging the type of a target region in the remote sensing image; and finally, re-selecting training samples to obtain new image classifiers, performing secondary detection on the to-be-detected remote sensing image, and performing comparison with first detection to finally determine cloud, snow and fog judgment results of the to-be-detected remote sensing image. An experimental result shows that the method can achieve relatively high detection precision.

Description

technical field [0001] The invention belongs to the field of quality detection of satellite remote sensing images, in particular to a method for detecting cloud, snow and fog of satellite remote sensing images based on a support vector machine. Background technique [0002] In optical satellite remote sensing images, remote sensing information is often affected by clouds, fog and snow. Clouds and snow will cover the surface information of the area where the image is located. Fog and haze will cover up many characteristic information in remote sensing images. Therefore, it is necessary to detect cloud, snow, and fog areas in remote sensing images, and to eliminate corresponding image data with excessive coverage of invalid information, so as to improve the utilization rate of optical satellite remote sensing images. [0003] The current remote sensing image cloud, snow, and fog detection methods mainly focus on the detection of clouds or fog, and the detection and identificat...

AI Technical Summary

Problems solved by technology

[0004] After searching the existing literature, it is found that the current cloud, snow, and fog detection methods have the following problems: First, the existing methods are difficult to detect clouds, snow, and fog at the same time

The detection method is affected by the type of detection, and a single detection method is difficult to adapt to a variety of detection requirements; the existing threshold method is not reliable, because the detection results are affected by the space-time type, it is difficult to extend to the more common detection; the feature extraction method selects Insufficient image feature information, the detection accuracy is not high enough

Second, the detection efficiency of cloud, snow, and fog detection methods is not high, and the complexity of the algorithm is high. It is difficult to quickly detect and identify the impact of big data. There are certain requirements for remote sensing data sources, and the universality is poor.

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