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Multispectral image classification method based on surface wave CNN

A multi-spectral image and classification method technology, applied in the field of multi-spectral image classification based on surface wave CNN, can solve the problem of multi-spectral image multi-scale, multi-direction, multi-resolution characteristics, multi-spectral image is difficult to get higher classification Accuracy and other issues to achieve the effect of improving classification accuracy

Active Publication Date: 2017-11-17
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

[0007] Since these feature extraction methods do not take into account the multi-scale, multi-directional, and multi-resolution characteristics of multispectral images, it is difficult to obtain high classification accuracy for multispectral images with complex backgrounds.

Method used

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  • Multispectral image classification method based on surface wave CNN
  • Multispectral image classification method based on surface wave CNN
  • Multispectral image classification method based on surface wave CNN

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Experimental program
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Embodiment

[0071] 1. Simulation conditions:

[0072] The hardware platform is: Intel(R) Xeon(R) CPU E5650@2.13GHz Multi-spectral image classification method based on multi-scale depth filter

[0073] Graphics card: Quadro K2200 / PCIe / SSE2, 2.40GHz multi-spectral image classification method based on multi-scale depth filter

[0074] Memory: 8G

[0075] The software platform is: Caffe.

[0076] 2. Simulation content and results:

[0077] Carry out experiments under the above-mentioned simulation conditions with the method of the present invention, namely randomly select 5% of marked pixels as training samples from each category of multispectral data respectively, and use the whole picture as test data to obtain the following: image 3 classification results.

[0078] from image 3 It can be seen that the regional consistency of the classification results is good, and the edges of different regions are also very clear after division, and the detailed information is maintained.

[0079]...

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Abstract

The invention discloses a multispectral image classification method based on surface wave CNN. To-be-classified multispectral images are inputted, normalization processing on the multispectral data is carried out to acquire a matrix, block taking based on a center pixel point is carried out for the matrix after normalization, and a training data set and a test data set are acquired; a classification model based on the surface wave CNN is constructed; the training data set is utilized to train the classification model; the trained classification model is utilized to classify the test data set. The method is advantaged in that a multi-scale depth filter is introduced, multispectral image classification precision is improved, and the method can be applied to target classification.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a multispectral image classification method based on surface wave CNN. Background technique [0002] Multispectral remote sensing is a remote sensing technology that uses sensors with more than two spectral channels to simultaneously image ground objects. It divides the electromagnetic wave information reflected by objects into several spectral segments for reception and recording. Multi-spectral remote sensing can not only distinguish ground objects according to the difference in image shape and structure, but also distinguish ground objects according to the difference in spectral characteristics, which expands the amount of remote sensing information. Both the multi-spectral photography used in aerial photography and the multi-spectral scanning used in land satellites can obtain remote sensing data of different spectral bands, and the images or data of sub-...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/24G06F18/214
Inventor 焦李成张文华马文萍杨淑媛侯彪刘芳尚荣华张向荣马晶晶张丹唐旭
Owner XIDIAN UNIV
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