A method and system for classification of small sample remote sensing objects based on feature generation network

Abstract

The present invention provides a small-sample remote-sensing object classification method and system based on a feature generation network, comprising: dividing the remote-sensing object classification data set into a source data set and a target data set based on the object type; The feature extractor and the generating network are trained to complete the parameter initialization of the feature extractor; based on the target data set, the feature extractor and the generating network are used to generate features, and the generated feature sets corresponding to each object type are obtained, which can effectively Expand high-quality target data to reduce the risk of overfitting on the target data set; then use the feature extractor to perform feature extraction on the test feature image, and perform test sample features and various feature types. The similarity measure gives the confidence score of the test image on various types of ground objects, and then determines the classification of the ground objects of the tested image, which solves the problem of classification of remote sensing ground objects under the condition of small samples.

Description

technical field [0001] The invention relates to the field of remote sensing intelligent recognition, in particular to a method and system for classifying small sample remote sensing objects based on a feature generation network. Background technique [0002] The deep learning method has been widely used in the remote sensing object classification task and has repeatedly refreshed the classification accuracy on the public data set. However, with the practical application of the deep learning method in the remote sensing object classification task, a major problem exposed is The severe dependence of model classification accuracy on huge training data. [0003] In the practical application of remote sensing intelligent recognition, the collection and labeling of remote sensing image data requires a certain amount of manpower, material resources and time, which forms a contradiction with the dependence of deep learning technology on massive data, mainly reflected in: [0004] (...

AI Technical Summary

Problems solved by technology

[0004] (1) For specific ground object scenes, such as regional remote sensing imaging under severe imaging weather and remote sensing data of a secret military base, it is not enough to collect thousands of high-quality remote sensing images for the training of deep learning models. practical;

[0005] (2) The training data set prepared before model deployment is often difficult to match the actual needs

Its defect is that it needs to train the deep generation model and the classification model separately, the learning efficiency is low, and the learning efficiency is low on small samples facing various ground object classification tasks, and it lacks the versatility of various ground object targets.

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