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Neighborhood Weighted Average Hyperspectral Image Classification Method Based on Deep Belief Network

A technology of deep belief network and hyperspectral image, which is applied in the direction of instruments, scene recognition, calculation, etc., can solve the problems of increasing pre-training and fine-tuning calculation time, and achieve the effect of low computer performance requirements, improved classification accuracy, and fast operation speed

Active Publication Date: 2019-04-12
HARBIN INST OF TECH
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  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to solve the shortcomings of the existing classification methods that increase the calculation time of pre-training and fine-tuning compared with pure spectral data, and propose a neighborhood weighted average hyperspectral image classification method based on deep belief networks

Method used

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  • Neighborhood Weighted Average Hyperspectral Image Classification Method Based on Deep Belief Network
  • Neighborhood Weighted Average Hyperspectral Image Classification Method Based on Deep Belief Network
  • Neighborhood Weighted Average Hyperspectral Image Classification Method Based on Deep Belief Network

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specific Embodiment approach 1

[0023] Specific implementation mode one: combine figure 1 To illustrate this embodiment, a specific process of a neighborhood weighted average hyperspectral image classification method based on a depth belief network (Depth Belief Networks, DBN) in this embodiment is as follows:

[0024] Step 1, extracting the spatial information of the hyperspectral raw data to obtain the spatial information of the hyperspectral raw data;

[0025] Step 2: Neighborhood weighting is performed on the spatial information of the hyperspectral raw data to obtain the processed hyperspectral data, namely the matrix M;

[0026] Step 3. Using the matrix M as the training set, the deep belief network is used for classification training.

specific Embodiment approach 2

[0027] Embodiment 2: The difference between this embodiment and Embodiment 1 is that in the first step, the hyperspectral raw data is extracted with spatial information to obtain the spatial information of the hyperspectral raw data; the specific process is:

[0028] First of all, because the PCA transformation will lose part of the nonlinear information, the PCA transformation is not performed on the hyperspectral image here, and the small square neighborhood is directly extracted for each pixel.

[0029] When the pixels to be classified in the hyperspectral raw data are not located at the edge of the hyperspectral remote sensing image, that is, there is a 5*5 square neighborhood around the pixels to be classified in the hyperspectral raw data, the pixels to be classified in the hyperspectral raw data Extract the spatial information of the pixels in the neighborhood of a 5*5 square, and obtain the spatial information of the original hyperspectral data;

[0030] When the pixel...

specific Embodiment approach 3

[0032] Specific embodiment 3: The difference between this embodiment and specific embodiments 1 or 2 is that in the step 2, the spatial information of the hyperspectral raw data is subjected to neighborhood weighting to obtain the processed hyperspectral data, namely the matrix M; specifically The process is:

[0033] The so-called neighborhood weighting method takes the pixel to be classified as the center point, and performs weighted average according to the distance between the pixel in the square neighborhood and the center point and the number of pixels;

[0034] Step 21, grouping pixels in a square neighborhood of a size of 5*5 around the pixel to be classified according to the pixel distance from the center point in a square neighborhood of a size of 5*5 around the pixel to be classified;

[0035] Step 22, determining the weight coefficient and the overall weight coefficient of each group of pixels;

[0036] Step two and three, after summing the internal pixels of each...

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Abstract

A neighborhood weighted average hyperspectral image classification method based on a deep belief network. The invention relates to a neighborhood weighted average hyperspectral image classification method based on a deep belief network. The purpose of the present invention is to solve the problem that the existing classification method increases the calculation time of pre-training and fine-tuning compared with pure spectral data. The specific process of a neighborhood weighted average hyperspectral image classification method based on a deep belief network is as follows: step 1, extract the spatial information of the hyperspectral raw Neighborhood weighting is carried out on the spatial information of the data to obtain the processed hyperspectral data, that is, matrix M; step 3, using matrix M as the training set, and using the deep belief network for classification training. The invention is used in the field of image classification.

Description

technical field [0001] The invention relates to a neighborhood weighted average hyperspectral image classification method based on a deep belief network. Background technique [0002] With the continuous development of remote sensing technology, the spectral information of hyperspectral images is very rich, and each pixel has information of hundreds of wavelengths. But it was found that traditional methods for multispectral images are not suitable for hyperspectral images. The main contradiction is that traditional methods cannot handle such high dimension of spectral data well. In order to solve this problem, in the past ten years, new methods have been introduced into hyperspectral image classification. Among them, the training cost of traditional methods is high, and the classification accuracy often decreases with the increase of feature dimension. Hughes phenomenon, or the curse of dimensionality. With the rapid development of deep learning software and hardware tech...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/00
CPCG06V20/13G06F18/24137G06F18/214
Inventor 林连雷杨京礼宋欣益董弘健
Owner HARBIN INST OF TECH
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