Automatic cloud detection method and system for multi-spectral remote sensing satellite image

Abstract

The present invention provides an automatic cloud detection method and system for a multi-spectral remote sensing satellite image. The method comprises: preparing data; roughly extracting clouds; extracting texture information on an image intensity information channel by combining histogram equalization and bilateral filtering; segmenting a texture information map by using a two-dimensional Otsu threshold; removing an error from a rough detection result by using a binary detail map after segmentation; using intensity information of a raw image as a guide map; and based on the rough detection result with the error being removed, performing accurate extraction on the clouds through edge seed expansion. The technical scheme actually realizes automatic, fast, and accurate detection of cloud.

Description

technical field [0001] The invention belongs to the field of surveying and mapping science and technology, and relates to a multi-spectral remote sensing satellite image automatic cloud detection technology based on threshold value segmentation and texture information. Background technique [0002] During the imaging process of satellite images, due to the influence of the atmosphere, there will be cloud occlusion, which will seriously affect the image quality. Thick clouds cause irreparable damage to ground spectral information. Although thin clouds do not completely cover ground feature information, they still cause spectral distortion of the original ground features, seriously affecting follow-up work such as ground feature interpretation and production of surveying and mapping satellite products. Therefore, realizing accurate cloud detection before production has important practical significance. [0003] At present, it mainly uses the characteristics of cloud spectrum,...

AI Technical Summary

Problems solved by technology

The texture feature method mainly uses the difference between the cloud and ground texture features. Generally speaking, the cloud texture details are obviously less than that of the ground, and the texture features are usually calculated by combining the fractal dimension and the second-order moment method with the block sub-image. The method needs enough training samples to obtain a reliable cloud feature interval to ensure the classification accuracy, resulting in low efficiency

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