Multi-scale feature fusion remote sensing image segmentation method, device, equipment and memory

A multi-scale feature, remote sensing image technology, applied in instruments, character and pattern recognition, computer parts and other directions, can solve the problems of remote sensing image segmentation effect influence and reduction

Active Publication Date: 2021-11-23
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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However, studies have shown that once a certain depth is reached, the benefit of adding more layers diminishes rapidly
Therefore, the limited receptive

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  • Multi-scale feature fusion remote sensing image segmentation method, device, equipment and memory
  • Multi-scale feature fusion remote sensing image segmentation method, device, equipment and memory
  • Multi-scale feature fusion remote sensing image segmentation method, device, equipment and memory

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[0022] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0023] In one embodiment, such as figure 1 As shown, a multi-scale feature fusion remote sensing image segmentation method is provided, which includes the following steps:

[0024] Step 100: Obtain high-resolution remote sensing images, and label the remote sensing images to obtain training samples.

[0025] Step 102: Construct a multi-scale feature fusion remote sensing image segmentation network.

[0026] The multi-scale feature fusion remote sensing image segmentation network includes an input network, an encoder, and a decoder based on multi-scale feature map fusion....

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Abstract

The invention relates to a multi-scale feature fusion remote sensing image segmentation method and device, equipment and a memory. The method comprises the following steps: collecting a remote sensing image, and marking to obtain a training sample; a multi-scale feature fusion remote sensing image segmentation network is constructed, and the network comprises an input network which is used for segmenting a training sample into small blocks with fixed sizes, expanding the small blocks into one-dimensional vectors and embedding position codes to obtain an input sequence; the encoder is used for extracting different levels of an input sequence by utilizing a multi-layer Transform module; the decoder is used for prediction result by fusing the multi-scale feature map; and training the network by using a training sample to obtain a trained multi-scale feature fusion remote sensing image segmentation model, and obtaining a prediction result of a to-be-detected remote sensing image by using the model. According to the method, a multi-scale feature map extracted by an encoder is fully utilized, local classification and hierarchical segmentation are combined, and the method can adapt to the characteristic that targets in remote sensing images are complex and changeable.

Description

technical field [0001] The present application relates to the technical field of remote sensing image processing, in particular to a multi-scale feature fusion remote sensing image segmentation method, device, equipment and memory. Background technique [0002] With the continuous development of remote sensing detection technology, a large amount of high-resolution remote sensing image data can be obtained. Semantic segmentation of remote sensing images is one of the means of processing remote sensing image data. This method has many applications in forest cover detection, urban change detection, urban planning, crop monitoring, etc. Remote sensing image segmentation is a specific task in semantic segmentation. Segmenting remote sensing images can extract rich information contained in it for researchers to use. Therefore, the performance of image segmentation determines the quality of information extraction. Remote sensing images contain rich category information and irregu...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/46G06K9/62
CPCG06F18/24G06F18/253G06F18/214
Inventor 王威唐琛王新刘冠群
Owner CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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