3dcnn-based spatial-spectral joint classification method for hyperspectral images

Abstract

The invention relates to a hyperspectral image space-spectrum joint classification method based on 3DCNN. Aiming at the characteristic that hyperspectral image data is a three-dimensional structure, a three-dimensional convolutional network suitable for hyperspectral images is constructed to complete hyperspectral image space-spectrum joint classification. First, from the original hyperspectral image, a data block in a certain neighborhood centered on the pixel to be classified is extracted as the initial spatial spectral feature, and the 3DCNN network constructed is trained in combination with the label of the pixel to be classified. Then, the hyperspectral image space-spectrum joint classification is accomplished using the trained 3DCNN. Beneficial effects are: 1) solve the complex processing problem of spectrum space dimensionality reduction or compression in the existing classification technology; Rich information and saves the trouble of artificially pre-setting features; 3) 3DCNN-based hyperspectral image space-spectrum joint classification method. 4) The hyperspectral image classification accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of remote sensing information processing, and relates to a hyperspectral image classification method, in particular to a 3DCNN-based hyperspectral image space-spectrum joint classification method. Background technique [0002] Hyperspectral remote sensing images have high spectral resolution, multiple imaging bands, and large amounts of information, and are widely used in remote sensing applications. Hyperspectral image classification technology is a very important content in hyperspectral image processing technology. It mainly includes two parts: feature extraction and classification. Among them, features are extracted from the original hyperspectral image. This step has a great impact on the classification accuracy of hyperspectral images: Classification The robustness of the features can greatly improve the classification accuracy; on the contrary, the classification features with poor robustness will sig...

AI Technical Summary

Problems solved by technology

[0004] However, the existing methods for extracting spatial spectral features of hyperspectral images using deep models are very complicated. First, it is often necessary to reduce the dimensionality of the original hyperspectral image in spectral space, and then The information after dimensionality reduction is combined with the spectral information to obtain the spatial spectral feature

Dimensionality reduction is computationally intensive, and certain spectral information is lost, affecting accuracy

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