3DCNN (three-dimensional convolutional neural network)-based high-spectral image space spectrum combined classification method

Abstract

The invention relates to a 3DCNN (three-dimensional convolutional neural network)-based high-spectral image space spectrum combined classification method. According to the feature that high-spectral image data is of a three-dimensional structure, the three-dimensional convolutional network suitable for a high-spectral image is constructed and high-spectral image space spectrum combined classification is completed. First of all, data blocks within a certain neighborhood scope, taking a pixel to be classified as a center, are extracted from an original high-spectral image as initial space spectrum features, and through combination with a label of the pixel to be classified, the constructed 3DCNN is trained. Then, by use of the trained 3DCNN, the high-spectral image space spectrum combined classification is completed. The method has the following advantages: 1, the problem of need of complex processing of spectrum space dimension reduction or compression in the prior classification arts is solved; 2, the 3DCNN suitable for the high-spectral image data of the three-dimensional structure is constructed, rich information of the high-spectral image is fully utilized, and the trouble of manual set of features in advance is omitted; 3, the 3DCNN-based high-spectral image space spectrum combined classification method enlarges the application scope of depth learning and also provides a new approach for high-spectral image classification; and 4, the high-spectral image classification precision 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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