Optical remote sensing image saliency target detection method

A technology for optical remote sensing image and target detection, which is applied in the field of remote sensing image processing and deep learning, and can solve the problems of not being able to detect it, not considering the interactive relationship of attention information, and the integrity of the saliency detection result is not high.

Pending Publication Date: 2021-02-09
BEIJING JIAOTONG UNIV
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Problems solved by technology

[0004] (1) The existing deep learning optical remote sensing saliency target detection method usually directly propagates and fuses multi-level features, and does not consider the interactive relationship between the attention information corresponding to each level of

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  • Optical remote sensing image saliency target detection method
  • Optical remote sensing image saliency target detection method

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Embodiment Construction

[0054]The present invention will be further described in detail below in conjunction with the drawings.

[0055]The present invention proposes a method for detecting saliency targets in optical remote sensing images based on a dense attention flow network, which mainly includes a feature encoding module guided by the attention flow and a progressive feature decoding module. The feature coding module guided by the attention flow passes the global context The perceptual attention module adaptively captures the long-distance global relationship, and further embeds it in the dense attention flow structure, so that the shallow attention cues can be transmitted to the deep layer, and then guide the generation of the deep attention feature map, so that the network can generate More accurate, complete and sharper significance detection results.

[0056]1. Technical route

[0057]The dense attention flow network proposed by the present invention is an encoder-decoder structure. Different from the tra...

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Abstract

The invention relates to an optical remote sensing image saliency target detection method, and the method comprises the steps: S1, building a dense attention flow network which comprises an attentionflow guided feature coding module and a progressive feature decoding module; s2, importing an optical remote sensing image; s3, processing the optical remote sensing image by adopting an attention flow guided feature coding module to generate enhanced features with higher discrimination capability, the attention flow guided feature coding module mainly comprising a global context perception attention module and a dense attention flow structure, and S4, decoding the enhanced features in the step S3 by adopting a progressive feature decoding module, gradually fusing the deep features and the shallow features in a feature decoding stage, and generating a plurality of side outputs and final outputs under the supervision of the saliency map and the saliency edge map.

Description

Technical field[0001]The invention belongs to the field of remote sensing image processing and deep learning, and relates to a method for detecting a significant target in an optical remote sensing image.Background technique[0002]Inspired by the human visual attention mechanism, the purpose of the visual saliency detection task is to detect the most concerned target or area in the input data (such as images, videos, etc.). It has been widely used in target detection, image editing, intelligent photography, and autonomous driving Many other fields have important research value and broad market prospects. However, the optical remote sensing image that the present invention focuses on has some properties that are different from the images taken by traditional handheld cameras (also called natural scene images) due to its special shooting method and imaging environment, which makes the direct transplantation of existing natural scene images significant Target detection methods often fai...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06N3/045G06F18/214
Inventor 丛润民张禹墨张晨杨宁杨浩巍赵耀
Owner BEIJING JIAOTONG UNIV
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