Wuhan University

Liangpei Zhang

Di Wang

Jiaqi Yang

Chen Wu

Yonghao Xu

Disclosed in the present invention is hyperspectral image classification method based on context-rich networks. The method comprises a training stage and a prediction stage, wherein the training stage comprises image pre-processing, sample selection and network training. Firstly, performing normalization on a hyperspectral image, and then randomly selecting an appropriate proportion of marked samples from each category to generate a label map, and performing training by using the designed network; in the prediction stage, directly inputting the whole image into the trained network and obtaining a final classification result. By means of the present invention, data pre-processing, feature extraction, the process of context-rich information capturing, and classification are taken into comprehensive consideration in the whole flow; and the classification of a hyperspectral image is realized by means of constructing an end-to-end network.

Hyperspectral image classification method based on context-rich networks

A hyperspectral remote sensing image classification method based on a self-attention context network is provided. The method constructs a spatial dependency between pixels in a hyperspectral remote sensing image by self-attention learning and context encoding, and learns global context features. For adversarial attacks in the hyperspectral remote sensing data, the proposed method has higher security and reliability to better meet the requirements of safe, reliable, and high-precision object recognition in Earth observation.

Hyperspectral remote sensing image classification method based on self-attention context network

Disclosed is a method for classifying hyperspectral images on the basis of an adaptive multi-scale feature extraction model, the method comprising: establishing a framework comprising the two parts of a scale reference network and a feature extraction network, introducing a condition gate mechanism into the scale reference network, performing determination step-by-step by means of three groups of modules, inputting features into a corresponding scale extraction network, deep mining rich information contained in a hyperspectral remote sensing image, effectively combining features of different scales, improving a classification effect, and generating a fine classification result map.

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Hubei, China

Liangpei Zhang