Airport target detection method for high-resolution remote sensing image in complex background

A technology of remote sensing images and complex backgrounds, applied to computer parts, instruments, biological neural network models, etc., can solve problems such as insufficient detection accuracy, achieve accurate classification results, improve accuracy, and improve reliability

Active Publication Date: 2018-09-28
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0005] In order to avoid the deficiencies of the prior art, the present invention proposes an airport target detection method for high-resolution remote sensing images under complex backgrounds, and overcomes the problem of insufficient detection accuracy of existing airport target detection methods

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  • Airport target detection method for high-resolution remote sensing image in complex background
  • Airport target detection method for high-resolution remote sensing image in complex background
  • Airport target detection method for high-resolution remote sensing image in complex background

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specific Embodiment

[0041]1. Significance detection

[0042] The full convolutional network is used to detect the saliency of the remote sensing image, and the pixel-level saliency feature map of the remote sensing image is calculated. The steps are as follows:

[0043] (1) Preprocess the remote sensing image, transform the image in equal proportions to make its minimum size 600, and subtract the statistical mean value from each pixel value.

[0044] (2) Select the fully convolutional network FCN32s as the network basis, set the output category to 2, and remove the softmax layer, directly use the upsampled features as salient features, and set the loss function to the cross entropy of categories and probabilities.

[0045] (3) Use the FCN32s trained on the training set Pascal VOC 2012Segmentation database as the pre-training network, and use the saliency data set MSRA-1000 for network optimization

[0046] (4) Pass the remote sensing image into the trained fully convolutional saliency detection ...

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Abstract

The present invention relates to an airport target detection method for a high-resolution remote sensing image in a complex background. The method comprises: using a full-convergence network to perform saliency detection on a remote sensing image, and using the improved LSD algorithm to extract line features of the remote sensing image; extracting saliency features and linear features to respectively divide the target candidate region, and taking the region simultaneously satisfying the saliency features and the linear features as a target candidate region; and extracting depth features of thecorresponding image in a candidate region by using the convolution network, converting the two-dimensional matrix features with different sizes into one-dimensional features with the same length by using the ROI Pooling network, calculating the target probability and the positional offset of the candidate region through two independent fully connected networks, and detecting an airport target inthe remote sensing image. According to the method provided by the present invention, priori knowledge such as the airport saliency, the parallel straight runway and the like is used to generate a small number of candidate regions, the difficulty of the test can be significantly reduced, the extracted candidate regions are more accurate, and the accuracy of detection and the positional accuracy ofmarking the airport region are improved.

Description

technical field [0001] The invention belongs to a method for detecting airport targets in remote sensing images, and relates to a method for detecting airport targets in high-resolution remote sensing images under complex backgrounds, in particular to a method for detecting airport targets in high-resolution remote sensing images with complex backgrounds. Background technique [0002] Traditional airport detection algorithms use airport-specific prior knowledge to extract airport areas, which mainly include airport detection algorithms based on line features and color texture features and airport detection algorithms based on salient features. The airport detection algorithm based on line features and color texture features uses the prior knowledge of airport runway shape and texture to extract corresponding features from the image, and then segments the airport area, and finally uses a classifier to filter out false targets in the segmented area. This type of method is simp...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/176G06V2201/07G06N3/045G06F18/24
Inventor 李映刘凌毅崔凡呼延烺
Owner NORTHWESTERN POLYTECHNICAL UNIV
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