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GCAM-based high-resolution SAR image airport runway area automatic detection method and system

A high-resolution, airport runway technology, applied to radio wave measurement systems, neural learning methods, measurement devices, etc., can solve problems such as training time for large data sets, and achieve the effect of automatic extraction

Pending Publication Date: 2020-12-15
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

For airport extraction in high-resolution SAR images, a prior art proposes a deep learning network MDDA (Mult-level and densely dual attention) for runway areas in high-resolution SAR images, which can achieve high-precision airport extraction, but requires relatively high Large dataset and long training time

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  • GCAM-based high-resolution SAR image airport runway area automatic detection method and system
  • GCAM-based high-resolution SAR image airport runway area automatic detection method and system
  • GCAM-based high-resolution SAR image airport runway area automatic detection method and system

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Embodiment Construction

[0035] Such as figure 1 As shown, the present embodiment is based on the GCAM high-resolution SAR image airport runway area automatic detection method comprising:

[0036] 1) Downsampling the high-resolution SAR image to generate a medium-resolution image;

[0037] 2) Input the medium-resolution image to the geospatial context attention mechanism network GCAM to extract the runway area;

[0038] 3) Carry out coordinate mapping on the extracted runway area to obtain the detection result of the final high-resolution SAR image.

[0039] In this embodiment, downsampling the high-resolution SAR image in step 1) specifically refers to performing 5 times downsampling processing on the SAR image by using a pixel value extraction method. It mainly includes two parts of downsampling: one is the downsampling of the sample pictures of the data set, and the other is downsampling of three high-resolution test SAR images, and the medium-resolution SAR images are obtained after sampling.

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Abstract

The invention discloses a GCAM-based high-resolution SAR image airport runway area automatic detection method and system. The GCAM-based high-resolution SAR image airport runway area automatic detection method comprises the steps of performing down-sampling on a high-resolution SAR image to generate a medium-resolution image; inputting the medium-resolution image into a geospatial context attention mechanism network GCAM to extract a runway area; and performing coordinate mapping on the extracted runway region to obtain an airport runway region detection result of the final high-resolution SARimage. Experiments show that compared with DeepLabV3 +, RefineNet and MDDA networks, the method is high in precision and short in consumed time, geographic space information of an SAR image airport can be fully learned, and high-precision, rapid and automatic extraction of a high-resolution SAR image airport runway area can be achieved.

Description

technical field [0001] The invention relates to an airport runway area automatic detection technology, in particular to a GCAM-based high-resolution SAR image airport runway area automatic detection method and system. Background technique [0002] Airports are important transportation hubs and military facilities, and airport target detection from Synthetic Aperture Radar (SAR) images has become an important application. SAR has the advantages of all-weather, all-weather imaging and penetration of clouds and fog. However, SAR images are less readable than optical images, and interpretation is more complicated. Therefore, most airport detections are usually based on optical remote sensing images. With the higher and higher resolution of SAR images and more and more data, in recent years, the research on using SAR images to extract airports has gradually increased, and the related research has also begun to deepen. Traditional airport extraction methods are time-consuming and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G01S13/90
CPCG06N3/08G01S13/9027G01S13/9094G06V20/13G06N3/045G06F18/253
Inventor 陈立福谭思雨潘舟浩邢进李振洪袁志辉邢学敏
Owner CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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