Remote sensing image sample intelligent collecting method

Abstract

The invention provides a remote sensing image sample intelligent collecting method. According to the method, a sample set required by image classification can be effectively selected, and sampling collecting time and money cost can be saved. The method comprises the steps that as for a remote sensing image to be classified, a few samples are randomly marked by a user; image classification is performed on images by means of the samples; classification results are converted into various types of probabilities; a sample set which is not marked and has the largest information amount is selected; the user performs type marking on the sample set which is not marked; the sample set just marked and the existing sample set constitute a new sample set; the images are trained again by using the new sample set; the process is performed in an iterated mode; iterating is stopped to obtain a set of samples when a certain condition is met.

Description

technical field [0001] The invention relates to remote sensing image processing technology, in particular to an intelligent remote sensing image sample collection method, which can greatly reduce the number of samples required for image classification, thereby reducing the cost of sample collection. Background technique [0002] Remote sensing technology has been widely used in many fields, such as forest resources planning, crop yield estimation, environmental monitoring, etc. Remote sensing image classification technology is a key step in converting remote sensing images from data to information. In terms of whether training samples are needed, remote sensing image classification methods can be divided into supervised classification and unsupervised classification. Supervised classification methods are most commonly used in remote sensing image classification. Supervised classification methods need to manually determine the classification system, training samples of each...

AI Technical Summary

Problems solved by technology

The main disadvantages of the above two sample collection strategies are: it is not possible to determine how many samples are needed to obtain a satisfactory classification effect; it is not known whether a good classification effect can be obtained with the existing number of samples

[0004] This patent proposes a new type of remote sensing image sample collection method; in view of the characteristics of time-consuming and labor-intensive sample collection in the field of remote sensing image processing, this patent proposes a sample that can predict very vague category information through the current classification results in the classification process , and then the sample collection staff will classify these sample points to form an updated training sample set, and the sample collection method of reclassification

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