Hyperspectral and panchromatic image fusion method for extracting spatial features based on AAE

A hyperspectral image and panchromatic image technology, used in image enhancement, image analysis, image data processing and other directions, can solve the problems of imperfect spectral information, sub-optimal, high algorithm complexity, reduce spectral distortion, solve the problem of space Insufficient enhancement, the effect of reducing spectral loss

Active Publication Date: 2019-12-03
XIDIAN UNIV
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Problems solved by technology

The disadvantage of this method is that the spectral information preservation is constrained by using the output of the spatial preservation network and the original image, that is, the spectral information preservation depends on the spatial information preservation, which is indirect and may lead to suboptimal preservation results.
Although this method obtains a fusion result with improved resolution by training the neural network, the method still has the disadvantage that it does not maintain th

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  • Hyperspectral and panchromatic image fusion method for extracting spatial features based on AAE
  • Hyperspectral and panchromatic image fusion method for extracting spatial features based on AAE
  • Hyperspectral and panchromatic image fusion method for extracting spatial features based on AAE

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Embodiment 1

[0040] Due to the limitation of incident light energy, there is a certain balance between the spectral resolution, spatial resolution and signal-to-noise ratio of remote sensing data obtained by different sensors. Hyperspectral imaging sensors can acquire hyperspectral images with hundreds of narrow spectral channels and have high spectral resolution. Since hyperspectral images have detailed spectral information, they have unique advantages in classification, detection, and identification. However, the spatial resolution of hyperspectral images is insufficient. When processing and utilizing images, it is necessary to obtain hyperspectral images with both high spatial and spectral resolutions.

[0041] Using neural networks for image fusion is usually to learn the mapping relationship between input images and reconstructed fusion images. It has great advantages in feature extraction, data representation and description of complex relationships, but the design of neural network ...

Embodiment 2

[0076] The hyperspectral and panchromatic image fusion method based on extracting spatial features against an autoencoder is the same as in Embodiment 1, and the training process of the network described in step 3 of the present invention includes the following steps:

[0077] The adversarial autoencoder can be divided into two major network structures: the autoencoder part and the discriminative part of the generative adversarial network (GAN). After initializing the weights and biases of the adversarial autoencoder, training the adversarial autoencoder is divided into two processes:

[0078] (3.1) Reconstruction of input samples: the upsampled hyperspectral image H is used as the input of the adversarial autoencoder for training, and the loss function of the autoencoder is calculated; the stochastic gradient descent algorithm is used for optimization, and the optimization process is to minimize the loss function The process of optimizing, while updating the network weights a...

Embodiment 3

[0082] The hyperspectral and panchromatic image fusion method based on extracting spatial features against the self-encoder is the same as that in Embodiment 1-2, and the panchromatic image is enhanced as described in step 4, specifically including the following steps:

[0083] (4.1) Perform adaptive histogram equalization on the panchromatic image P:

[0084] P h =adapt thisteq(P)

[0085] Among them, P represents a panchromatic image, adaptthisteq( ) represents an adaptive histogram equalization function, and P h Represents a panchromatic image after adaptive histogram equalization;

[0086] (4.2) Use the Laplacian-Gaussian (LOG) enhancement algorithm to increase the panchromatic image P after adaptive histogram equalization h space details.

[0087] (4.2.1) Use the Gaussian filter to remove the noise of the panchromatic image after adaptive histogram equalization:

[0088] P g =P h × g

[0089] Among them, P h Represents the panchromatic image after adaptive histog...

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Abstract

The invention discloses a hyperspectral and panchromatic image fusion method for extracting spatial features based on AAE, which solves the problems of serious spectral distortion and insufficient spatial detail injection in the existing hyperspectral image fusion, and comprises the following implementation steps of: obtaining an image data set for preprocessing; constructing and training a hyperspectral image spectrum constraint adversarial auto-encoder network; extracting spatial features of the hyperspectral image; acquiring space details of the enhanced panchromatic image; fusing the spatial information of the hyperspectral image and the enhanced panchromatic image; constructing a gain matrix; and obtaining a hyperspectral image with high spatial resolution. Hyperspectral and panchromatic image space information is considered at the same time, deep space features are extracted through AAE, spectral loss is effectively reduced, and the space resolution is effectively improved. Simulation proves that the hyperspectral image fusion method is better in fusion index and visual effect space and spectral performance, better in space detail maintenance and more perfect in spectral information, and is used for hyperspectral image fusion processing.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to fusion of hyperspectral images, in particular to a fusion method of hyperspectral images and panchromatic images based on extraction of spatial features by an adversarial autoencoder (AAE). The invention can be used to obtain hyperspectral images with high spatial resolution and rich spectral information. Background technique [0002] Images with high spatial resolution are conducive to precise positioning of targets, and images with high spectral resolution are conducive to distinguishing categories of different features and accurately identifying targets. Hyperspectral images have more bands in a certain wavelength range and high spectral resolution. They have been used in many fields and some practical applications, such as vegetation research, precision agriculture, regional geological filling images, mineral exploration and environmental monitoring, etc. . L...

Claims

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Application Information

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IPC IPC(8): G06T5/50G06T3/40G06K9/62G06N3/04G06N3/08
CPCG06T5/50G06T3/4023G06T3/4076G06N3/08G06T2207/10036G06N3/045G06F18/253
Inventor 谢卫莹钟佳平李云松雷杰刘保珠
Owner XIDIAN UNIV
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