Hyperspectral image classification method based on spectral-spatial cooperation of deep convolutional neural network

Abstract

The present invention relates to a hyperspectral image classification method based on spectral-spatial cooperation of a deep convolutional neural network, which leads the conventional deep convolutional neural network applied to a two-dimensional image into the three-dimensional hyperspectral image classification problem. Firstly, the convolutional neural network is trained by using a small volume of label data, and a spectral-spatial feature of a hyperspectral image is autonomously extracted by using the network without carrying out any compression and dimensionality reduction processing; then, a support vector machine (SVM) classifier is trained by using the extracted spectral-spatial feature so as to classify an image; and finally, the trained neural network is combined with the trained classifier, the neural network extracts a spectral-spatial feature of a to-be-classified target and the classifier determines a specific category of the extracted spectral-spatial feature so as to acquire a structure (DCNN-SVM) that can autonomously extract the spectral-spatial feature of the hyperspectral image and carry out classification to the spectral-spatial feature, thereby forming a set of hyperspectral image classification method.

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 hyperspectral image classification method based on a space-spectrum combination of a deep convolutional neural network. 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 plays an important role in these applications. The features used for classification are extracted from the original hyperspectral image. This step has a great impact on the classification accuracy of hyperspectral images. Improve the classification accuracy; on the contrary, the classification features with poor robustness will significantly reduce the classification effect. [0003] In recent years, deep learning has made...

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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