Remote sensing image scene classification method based on semi-Bayesian deep learning based on Markov chain Monte Carlo and variational inference

Abstract

The invention discloses a semi-Bayesian deep learning remote sensing scene classification method based on Markov chain Monte Carlo and variational inference. First, build a half-Bayesian deep convolutional neural network. For the convolutional layer, only half of the convolutional layers are used to represent the weight parameters using the Gaussian distribution method, and the weight parameters of the fully connected layer are represented by the Gaussian distribution method. . Secondly, the approximate weight parameter distribution is initially obtained by using variational inference method. Then, the approximate weight parameter distribution is further improved iteratively by using the Markov chain Monte Carlo method and the variational contrast divergence method, and a more accurate approximate weight parameter distribution is obtained. The present invention utilizes the semi-Bayesian deep learning method, takes part of the weight parameters in the deep convolutional neural network as random variables, introduces uncertainty into the network model, avoids the phenomenon of overfitting, and enhances the robustness of the network model It has good performance in remote sensing image scene classification applications.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a semi-Bayesian deep learning remote sensing scene classification method based on Markov chain Monte Carlo and variational inference. Background technique [0002] Remote sensing image classification is one of the important research directions in the field of remote sensing, which is widely used in many application fields such as geological survey, disaster monitoring, traffic supervision and global temperature change. Therefore, in-depth research on remote sensing image classification has important application value for the future development of society. There are many kinds of classification algorithms for remote sensing images, such as ISODATA, K-means, minimum distance, maximum likelihood and other algorithms. According to the principle and focus of the above classification algorithms, they are divided into supervised and unsupervised, parametric and non-parametri...

AI Technical Summary

Problems solved by technology

[0008] (1) Feature extraction is complex and inefficient: Traditional classification methods need to use different image feature extraction algorithms to extract various types of features of images, and the process is cumbersome and complicated

At the same time, these classification algorithms generally only have a shallow structure, and the learned expression features cannot fully cover remote sensing image information, there is redundant information, and their classification performance and generalization ability are obviously insufficient.

[0009] (2) Lack of measurement of prediction uncertainty: the existing convolutional neural network model applied to remote sensing classification uses the method of point estimation to represent the weight, which can better fit the training image samples after a large number of training, but However, it will cause overfitting on the test image sample and cannot correctly predict the image label

[0010] (3) Variational inference shows that there is a deviation between the approximate distribution function and the real distribution function: for the Bayesian estimation method, the variational inference method is generally used to approximate the distribution function, but the approximate distribution function cannot be guaranteed to be similar to the real distribution function The effect of the algorithm is not robust enough, and the classification performance is poor

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