Feature pyramid-based remote-sensing image time-sensitive target recognition system and method

Abstract

The invention discloses a feature pyramid-based remote-sensing image time-sensitive target recognition system and method. The system comprises a target feature extraction sub-network, a feature layersub-network, a candidate area generation sub-network and a classification and regression sub-network, wherein the target feature extraction sub-network is used for carrying out multiple layers of convolution processing on a to-be-processed image and outputting the convolution processing result of each layer as a feature layer; the feature layer sub-network is used for overlapped the last feature layer and the current feature layer to obtain the current fused feature layer, wherein the topmost fused feature layer is a topmost feature layer; the candidate area generation sub-network is used forextracting candidate areas from different layers of fused feature layers; and the classification and regression sub-network is used for mapping the candidate areas to different layers of fused featurelayers so as to obtain a plurality of mapped fused feature layers, and carrying out target judgement on the plurality of mapped fused feature layers so as to output a result. According to the systemand method, the hierarchical structures of feature pyramids are utilized to ensure that all the scales of features have rich semantic information.

Description

technical field [0001] The invention belongs to the field of remote sensing image processing, and more specifically relates to a time-sensitive target recognition system and method for remote sensing images based on feature pyramids. Background technique [0002] Large-format remote sensing image moving target detection is an important component in the field of remote sensing image analysis and is extremely challenging due to its characteristics of multi-scale, wide format, and special viewing angle. Its basic task is to detect a given aerial or satellite image Determine whether it contains one or more category target objects and the precise location of each category object target. As an important part of computer vision and remote sensing image analysis, time-sensitive target detection in large-format remote sensing images has high research significance. With the rapid development of contemporary remote sensing technology, sensor technology and Internet technology, people ...

AI Technical Summary

Problems solved by technology

[0004] Existing deep learning target detection models usually only use the upper layer of single-scale features. Although the semantic information of the upper layer features is strong, the position information is weak, and the detection effect on multi-scale targets is poor. At the same time, the existing network scale has large parameters. Many, high demand for computing resources

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