High-resolution remote sensing image scene multi-label classification method based on multi-packet fusion

Abstract

The invention discloses a high-resolution remote sensing image scene multi-label classification method based on multi-packet fusion. The method comprises the following steps: firstly, extracting multiple heterogeneous features on a high-resolution remote sensing image according to grid division and performing encoding; secondly, dividing sub-regions through a layering method and a segmentation method to pool the coded features, and obtaining a layering example package and a segmentation example package; using Mahalanobis distance to cluster the packets by a K-Medoids method, solving distancesfrom the packets to all clustering centers, and forming vectors by all distance values, so as to convert a multi-instance packet into a single instance; carrying out series fusion on the obtained single examples; and finally, designing a plurality of binary classifiers through a pair of other methods to solve the multi-label problem. According to the multi-packet fusion-based high-resolution remote sensing image scene multi-label classification method provided by the invention, the classification performance is improved, and a more excellent classification result is obtained compared with an existing classification method.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and in particular relates to a multi-label classification method for high-resolution remote sensing image scenes based on multi-packet fusion. Background technique [0002] With the continuous deepening of people's research, remote sensing image classification technology has made significant progress. However, the previous research on classification technology is often based on a single label. This classification method has the advantage of being simple and clear, but at the same time there are also incomplete The downside of describing image content. In recent years, many researchers have also begun to realize this situation, and introduced the idea of ​​multi-label classification to solve such problems, one of which is called Multi-Instance Multi-Label learning (Multi-InstanceMulti-Label learning, MIML). Frames in particular attract attention. In MIML, an imag...

AI Technical Summary

Problems solved by technology

This method generates examples by selecting a variety of heterogeneous features, and then constructs hierarchical example packages and segmented example packages to realize the complementarity of package information. Finally, the multi-label classification problem of complex remote sensing scenes is solved through the multi-instance multi-label learning framework, and the improvement is improved. Multi-label classification performance

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