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A hyperspectral unknown target detection method based on evm and deep learning

A technology of target detection and deep learning, applied in the field of hyperspectral unknown target detection, to achieve the effects of wide application range, high real-time performance, and simple operation

Active Publication Date: 2022-06-07
NAT UNIV OF DEFENSE TECH
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  • Abstract
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  • Application Information

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Problems solved by technology

[0007] Aiming at the problem of hyperspectral unknown target detection that needs to be solved and the shortcomings of the current proposed method, the present invention proposes a hyperspectral unknown target detection method based on EVM and 3DCNN

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  • A hyperspectral unknown target detection method based on evm and deep learning
  • A hyperspectral unknown target detection method based on evm and deep learning
  • A hyperspectral unknown target detection method based on evm and deep learning

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Embodiment Construction

[0038] The present invention will be further described in detail below with reference to the accompanying drawings and specific implementation examples.

[0039] figure 1 The overall flow chart of the hyperspectral unknown target detection algorithm is shown. First, the hyperspectral image data is preprocessed and the data set is divided, then the test sample data is input into the 3DCNN network for training, and the training model is saved and the 3DCNN model is saved. The output of the last fully connected layer is used as the feature vector of the corresponding sample. The output feature vector obtains the weibull probability model corresponding to each sample through the EVM algorithm, and obtains the final EVM model composed of the feature vector corresponding to the sample, the label and the weibull probability model by reducing the model. Finally, the preprocessed test sample data is input into the 3DCNN model and the EVM model, and the probability of belonging to a kn...

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Abstract

The invention belongs to the field of hyperspectral intelligent perception, and discloses a hyperspectral position target detection method based on EVM and deep learning. Known class and unknown class, and divide the data set into training set and test set; S2: Input the preprocessed test sample data into the 3DCNN network for training, save the training model and output the classification feature vector; S3: All training The feature vector of the data and the corresponding category label use the EVM algorithm to establish an EVM model based on the Weibull distribution; S4: Input the preprocessed test sample data into the 3DCNN model and the EVM model, and calculate the probability of belonging to a known class or an unknown class ; The present invention has a clear structure and is easy to implement, and effectively improves the detection accuracy of unknown objects under the premise of ensuring the classification accuracy of known classes.

Description

technical field [0001] The invention mainly relates to the field of hyperspectral intelligent perception, in particular to a hyperspectral unknown target detection method based on EVM and deep learning. Background technique [0002] Hyperspectral image is a three-dimensional spectral image with spectral resolution in the range of 10l order of magnitude, including two-dimensional geometric space and one-dimensional spectral information of ground objects, realizing the integration of atlas. Hyperspectral images have a spectral resolution of nanometers, which can accurately depict the reflection spectrum of different objects, effectively improving the ability to process hyperspectral image classification and recognition. Classification and recognition of ground objects is one of the research focuses of hyperspectral data processing. Hyperspectral image classification is a process of distinguishing and confirming pixels in an image based on the classification of remote sensing ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/10G06V10/764G06V10/774G06V10/77G06V10/82G06K9/62G06N3/04G06N3/08G06N7/00
CPCG06N3/08G06V20/194G06V20/13G06V2201/07G06N7/01G06N3/045G06F18/2135G06F18/214G06F18/24Y02A40/10
Inventor 江天吴露婷彭元喜周侗刘煜
Owner NAT UNIV OF DEFENSE TECH