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Aerial remote sensing image recognition method based on multilayer and regional feature fusion

A technology of regional features and remote sensing images, which is applied in the field of aerial remote sensing image recognition to achieve the effect of reducing computational overhead, parameter quantity, and high accuracy.

Pending Publication Date: 2022-08-09
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0008] The invention provides an aerial remote sensing image recognition method based on the fusion of multi-layer and regional features, which solves the problems existing in the aerial image recognition task in the prior art. Based on the Resnet-50 backbone network, the main body uses the attention mechanism method to perform image features In aerial image recognition tasks, better recognition results can be obtained

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  • Aerial remote sensing image recognition method based on multilayer and regional feature fusion
  • Aerial remote sensing image recognition method based on multilayer and regional feature fusion
  • Aerial remote sensing image recognition method based on multilayer and regional feature fusion

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Embodiment Construction

[0052] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0053] like figure 1 As shown in the figure, an aerial remote sensing image recognition method based on multi-layer and regional feature fusion, the method is based on the Resnet-50 backbone network, and the subject adopts the attention mechanism method to process image features, which specifically includes the following steps:

[0054] Step (1) Aerial image data set production: preprocess the data set, randomly crop the image after filling, and perform random rotation and horizontal flip for data enhancement;

[0055] Step (2) build an image recognition model: based on the aerial image data set, train the image recognition model;

[0056] Step (3) Test image detection process: use the trained image recognition network and network weight parameters to identify the images in the test image, and output the predicted category.

[0057] Specifi...

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Abstract

The invention discloses an aerial remote sensing image recognition method based on multilayer and regional feature fusion, belongs to the field of fine-grained image recognition, and adopts a backbone network Resnet-50 to perform targeted improvement according to the characteristics of high updating speed, fuzzy image details, low resolution and the like of aerial remote sensing images. According to the method, a new multilayer fusion and regional feature fusion mode is adopted, attention weights are attached to multilayer fusion, the capability of autonomously distributing fusion proportions is given to the model, selection of detail parts in a shallow feature map is flexible, utilization of local detail feature information of a remote sensing image is better reserved, and the method is suitable for remote sensing image fusion. The extraction strategy of the region of interest reduces the calculation overhead and parameter quantity of region feature fusion.

Description

technical field [0001] The invention belongs to the field of fine-grained image recognition, in particular to an aerial remote sensing image recognition method based on multi-layer and regional feature fusion. Background technique [0002] In recent years, image recognition has been widely used in all aspects of life, including face recognition, flower recognition software, similar commodity query, etc., making people's life more convenient. However, with the gradual deepening of the application of image recognition, image recognition is not limited to the recognition of daily objects, and the recognition of images with different perspectives and types such as aerial remote sensing images has become an urgent problem to be solved in current research. [0003] At present, feature learning has been proved to have better recognition effect in the field of fine-grained image recognition. There are currently two research directions in feature learning. The first is that the image...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/10G06K9/62G06N3/04G06N3/08G06V10/25G06V10/44G06V10/774G06V10/80G06V10/82
CPCG06N3/08G06N3/045G06F18/214G06F18/253
Inventor 孙涵刘宇泽李明洋王恩浩康巨涛
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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