Remote sensing image multi-class target detection method based on sample reweighting

Abstract

The invention provides a remote sensing image multi-class target detection method based on sample reweighting. The method comprises: firstly, performing image data augmentation processing and scale zooming preprocessing; then, constructing a target detection network, the target detection network comprising a feature extraction module, a feature enhancement module and a detection head module, and performing feature enhancement operation on part of feature hierarchies in order to achieve significance expression of features; then, performing a network end-to-end training process, and guiding thetraining network to pay more attention to more target samples with large aspect ratio difference by adopting a sample reweighting strategy so as to optimize the training model; and finally, realizinga target detection process, inputting a to-be-detected remote sensing image into the trained target detection network to obtain a category prediction value and a coordinate offset of each priori frame, and filtering out a detection result with a relatively high overlapping rate for the same target by using non-maximum suppression. The method has high remote sensing image target detection precisionand speed.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a method for detecting multiple types of targets in remote sensing images based on sample reweighting, which can be used to improve the detection effect of target categories with large aspect ratio differences in remote sensing image data sets. Background technique [0002] Remote sensing image target detection is a key technology in the application field of remote sensing big data information. The close combination of high-resolution remote sensing image data and geographic information systems will be used in future urban road planning, engineering project evaluation, and monitoring and evaluation of renewable resources. There will be broad prospects for development. With the advent of the era of big data and the substantial improvement of computer hardware performance, the target detection algorithm based on deep learning technology has broke...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem of large difference between the salient feature extraction and the aspect ratio of target samples in the target detection task of optical remote

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