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Deep neural network space spectrum classification method for high-spectral image

A deep neural network and hyperspectral image technology, which can be used in instruments, scene recognition, computing, etc., can solve the problems of information loss, lack of consideration of target pixel spatial information, and insufficient classification accuracy, and achieve the effect of good classification accuracy.

Inactive Publication Date: 2017-03-22
CHONGQING UNIV
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AI Technical Summary

Problems solved by technology

[0005] Other shallow classifiers such as support vector machines, multi-classification logistic, etc., although the model is more concise, but the classification accuracy is not high enough
[0006] Existing deep learning algorithms such as the deep belief network do not consider the spatial information around the target pixel, while the 2-D deep convolutional network takes the spatial information into account, but its convolution kernel parameters are the same, which will undoubtedly bring loss of information

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Embodiment Construction

[0028] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0029] figure 2 It is a schematic flowchart of the method of the present invention. As shown in the figure, the deep neural network spatial spectrum classification method for hyperspectral images provided by the present invention specifically includes the following steps:

[0030] Step 1: Read hyperspectral remote sensing image data and normalize the original data;

[0031] Step 2: Extract the feature value of the target pixel point and the feature value of the domain pixel in the same band as the target pixel point to form a grouping feature;

[0032] Step 3: Integrate the grouping features of each band of the target pixel point to obtain the grouping spatial spectral features of the hyperspectral;

[0033] Step 4: Determine the sample category according to the target pixel category, and randomly divide the marked samples into training ...

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Abstract

The invention relates to a deep neural network space spectrum classification method for a high-spectral image and belongs to the technical field of deep learning and high-spectral remote sensing image classification. In the method, grouped space spectral features are used as input, according to input grouping features, a regularization item is added to an optimization target at a first layer of a deep neural network, and extraction of the space spectral features and waveband selection are realized. The method takes algorithm features of a deep belief network into consideration, also takes features of space information into consideration, performs individual processing on space groups of each waveband and is different from a deep convolutional network in which parameters in a convolutional nucleus are the same; and the algorithm can automatically attenuate weights of wavebands having quite small classification effects, realizes adaptive feature extraction and waveband selection, can obtain better classification accuracy compared to the typical deep belief network and has wide application prospect.

Description

technical field [0001] The invention belongs to the technical field of deep learning and hyperspectral remote sensing image classification, and relates to a deep neural network spatial spectrum classification method for hyperspectral images. Background technique [0002] With the continuous improvement of hyperspectral remote sensing image sensor technology, the spatial resolution and spectral resolution of hyperspectral remote sensing images have been greatly improved, whether on spaceborne or airborne, which makes the application of hyperspectral remote sensing images more and more widely. At the same time, with the improvement of spatial resolution and spectral resolution, it has also brought about a sharp increase in data dimensions, which can reach hundreds of dimensions. thousands of dimensions. This makes the previous algorithms that have good performance in low-dimensional space face great challenges in high-dimensional space. [0003] At present, the main methods...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/13G06F18/2415
Inventor 周喜川李胜力徐琅唐坊胡盛东刘书君张新征
Owner CHONGQING UNIV
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