Oil spill hyperspectral image detection method based on two-way graph u-net convolutional network

Abstract

The invention discloses an oil spill hyperspectral image detection method based on a two-way graph U-NET convolutional network, comprising the following steps: Step 1, performing graph structuring on the data of the hyperspectral image that needs to detect oil spill, and obtaining the spectrum Graph structure information and spatial graph structure information; Step 2, send described spectrogram structure information and spatial graph structure information into one way in the two-way graph U-NET convolutional network respectively, obtain the spectrogram of described hyperspectral image respectively feature and spatial map feature; step 3, fusion of the spectrogram feature and the spatial map feature to obtain the space-spectrum feature; step 4, sending the space-spectrum feature into the classifier to obtain the Classification results of hyperspectral images. The invention can map the European image data to non-European data, more effectively represent spectral information and spatial information, further extract the space-spectral feature of the oil spill hyperspectral image, and improve the detection accuracy of the oil spill hyperspectral image.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to the technical field of machine learning and hyperspectral image classification, and more specifically, to a hyperspectral image detection method based on a Dual Graph (Dual Graph) U-NET convolutional network, which can be used in the detection of oil spill hyperspectral images. Background technique [0002] Hyperspectral imaging refers to the use of imaging spectrometers to record the spectral characteristics of various ground objects, image each band separately, and form images of different bands into a data cube as a hyperspectral image. Hyperspectral images add spectral information on the basis of two-dimensional spatial information, and each pixel has a reflection spectral curve corresponding to it. Hyperspectral remote sensing has the ability to effectively locate and distinguish seawater and oil films due to its continuous spectral resolution and ability t...

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Problems solved by technology

However, the local information of high spatial resolution generated in the process of graph convolution is not fully utilized

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