Remote sensing scene classification method and system based on homogeneity and heterogeneity Transformers

Abstract

The invention discloses a remote sensing scene classification method and system based on homogeneity and heterogeneity Transformers. An input remote sensing scene picture is divided into a heterogeneous Patch and a homogeneous Patch by using a direct division method and a superpixel segmentation method respectively; a heterogeneous feature learning branch and a homogeneous feature learning branch are used to simultaneously extract heterogeneous information and homogeneous information in the divided heterogeneous Patch and the homogeneous Patch, and global information, local knowledge and related context information in a remote sensing scene are extracted based on a Transform structure to obtain a heterogeneous feature he-token0 and a homogeneous feature ho-token0; the heterogeneous feature he-token0 and the homogeneous feature ho-token0 are fused, the feature discrimination is enhanced by combining a metric learning mechanism, and finally remote sensing scene classification is completed according to the fused features. The method comprehensively extracts the feature representation of the remote sensing image, and has better remote sensing scene classification performance.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a remote sensing scene classification method and system based on homogeneous Transformer. Background technique [0002] Remote sensing scene classification plays a vital role in the field of remote sensing and has received increasing attention due to its wide range of applications. Existing remote sensing scene classification techniques are mainly divided into two categories. [0003] The first category is remote sensing scene classification methods based on feature extractors and classifiers. The features they extract mainly include low-level features (such as texture features, shape features, and color features) and middle-level features (such as bag-of-words models), and then use classifiers to complete remote sensing scene classification tasks for the extracted features. Commonly used classifiers include support Vector machines and decision trees, etc. ...

AI Technical Summary

Problems solved by technology

However, the similarity between remote sensing scenes, which helps to distinguish within / between samples, is not considered in depth

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