Satellite remote sensing image cloud amount calculation method on the basis of random forest

Abstract

The present invention discloses a satellite remote sensing image cloud amount calculation method on the basis of random forest. The satellite remote sensing image cloud amount calculation method on the basis of random forest comprises six steps: sample acquisition, feature extraction, image classifier training, segmentation of image to be measured, image classification, cloud amount calculation and the like. Through adoption of the method provided by the invention, multiple detections may be performed after training just once, an image classifier is obtained through a large number of image trainings, and the image classifier may be used again when cloud detection is performed. The random forest algorithm is low in time complexity at the prediction classification stage, and the cloud zone detection may be rapidly carried out. Through the test, the method provided by the invention is applicable to panchromatic images (ten-dimensional characteristic vector) and also applicable to n-channel multispectral images (10n-dimensional characteristic vector), and has been applied to an actual quality control system of satellite image products, so that the cloud detection of remote sensing images of multiple domestic satellites such as the resource satellite-3, mapping satellite-1, GF-1 and the like are performed, wherein the accuracies reach, respectively, 91%, 88% and 92.4%.

Description

technical field [0001] The invention belongs to the technical field of quality inspection of satellite remote sensing images, and in particular relates to a cloud amount calculation method for satellite remote sensing images based on random forests. Background technique [0002] In satellite remote sensing images, the existence of cloud areas will have a great adverse effect on the image quality and subsequent information processing. Therefore, the detection and identification of cloud cover is one of the main problems in the application field of satellite remote sensing images. The remote sensing cloud detection technology can be used to delete the data in the area where the cloud is located in the satellite remote sensing image, greatly reducing the amount of data, and avoiding the invalid data with excessive cloud cover occupying the storage space, processing power and transmission bandwidth of the system. It has on-orbit applications on satellites There are two applicati...

AI Technical Summary

Problems solved by technology

The spectral threshold-based method is based on the reflection characteristics and temperature characteristics of the cloud itself, using the reflectivity of the cloud under different band spectra, and artificially setting the spectral threshold to detect, but the actual cloud area is affected by factors such as seasons, atmospheric environment, and geographical location. The instability of the method makes the method too complex and not strong in adaptability; the method based on the image feature extracts the gray scale, frequency, texture and other features of the image, and detects the cloud through the classification of the features contained in the cloud image, but due to the cloud and ground objects There are overlapping phenomena in some features, and the detection results depend on the validity, weak correlation, and integrity of the selected features; the comprehensive method uses the spectral threshold method for initial detection, screens out candidate cloud areas, and then uses the method of feature extraction to analyze These areas are cloud detected again

[0004] The current cloud detection method has the following problems: First, the on-orbit cloud detection method has relatively large constraints on the volume, weight, and power consumption of the equipment, which limits the complexity and adaptability of the algorithm, and cannot guarantee an ideal cloud detection effect; Second, the existing threshold method is only for a certain satellite, lacks a general system method, has poor versatility, and the detection results are affected by the space-time type, so the reliability is not high; third, the existing methods based on image features , only a single texture or brightness or frequency feature is applied, and there are many deficiencies in the integrity of the selected features, which leads to the poor adaptability of the detection method. The detection effect on thick clouds is good, but for the detection of thin clouds and low clouds Difficulty still exists

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