Feature fusion method based on stack-type self-encoder

Abstract

The invention provides a feature fusion method based on a stack-type self-encoder. The technical scheme of the invention comprises the following steps: firstly extracting binary mode texture features of three local spots of an image, selecting and extracting a plurality of types of baseline features of the image through a feature selection method, carrying out the series connection of all obtained features, and obtaining a series vector; secondly carrying out the standardization and whitening of the series vector; enabling a whitened result to serve as the input of SAE, and carrying out the training of the SAE through employing a layer-by-layer greedy training method; finally carrying out the fine tuning of the SAE through the trained SAE and a softmax filter, and enabling a loss function to be minimum, wherein the output of the SAE is a fusion feature vector which is high in discrimination performance. The selected feature redundancy is small, and more information is provided for feature fusion.

Description

technical field [0001] The invention belongs to the technical field of image fusion, and relates to a feature fusion method based on SAE (Stacked Autoencoder, stacked autoencoder), which improves the discrimination and efficiency of fusion features. Background technique [0002] Feature fusion refers to the technology of comprehensive analysis and fusion processing of the extracted feature information. In image understanding, the use of feature fusion can not only increase the feature information of the image, but also effectively integrate the advantages of the original features to obtain a more comprehensive feature expression of the target. Classical feature fusion algorithm (feature fusion algorithm can refer to Wang Dawei, Chen Dingrong, He Yizheng. A review of multi-feature image fusion technology for target recognition[J]. Avionics Technology, 2011,42(2):6-12.), directly Combining features directly in a certain way does not essentially consider the influence of the r...

AI Technical Summary

Problems solved by technology

[0004] Existing SAE-based feature fusion algorithm (SAE-based feature fusion algorithm can refer to the literature Chen Y, LinZ, Zhao X, et al..Deep Learning-Based Classification of Hyperspectral Data[J].IEEE Journal of Selected Topics in Applied Earth Observations&RemoteSensing,2014,7(6):2094-2107.), the selected feature dimension is high, the fusion network structure is complex, the training time is long, and it cannot meet the real-time requirements

In addition, the redundancy between the features is large, and the complementarity is small, and the feature discrimination is not significantly improved after fusion.

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