Hyperspectral open set classification method based on self-supervised learning and multi-task learning

Abstract

The invention discloses a hyperspectral open set classification method based on self-supervised learning and multi-task learning, which mainly solves the problem of low classification precision caused by the fact that an existing hyperspectral open set classification method cannot fully utilize unlabeled samples of a hyperspectral open set, and the implementation scheme of the hyperspectral open set classification method comprises the following steps: inputting a hyperspectral image and preprocessing the hyperspectral image; performing neighborhood block taking on the preprocessed image to generate a training data set and a test data set; constructing a neural network model based on self-supervised learning and multi-task learning; training the constructed neural network model by utilizing the training data set and adopting a self-supervised learning method and a multi-task learning method; and inputting the test data set into the trained neural network model to obtain a classification result. According to the method, label-free sample information can be fully utilized, the problem that label samples are few is solved, the classification precision is improved, and the method can be applied to environment monitoring, resource exploration, urban planning and agricultural planning.

Description

technical field

[0001] The invention belongs to the technical field of image processing, and further relates to a hyperspectral open set classification method, which can be applied to environmental monitoring, resource exploration, urban planning and agricultural planning. Background technique

[0002] Hyperspectral records the continuous spectral characteristics of ground objects with its rich band information, and can perform more types of ground object recognition and higher-precision target classification. Recently, with the wide application of deep learning in various fields, a variety of deep learning classification methods have emerged in the field of hyperspectral image classification, such as autoencoders, convolutional neural networks, and deep belief networks. Moreover, the proposal of the deep learning classification method with unknown class recognition ability also alleviates the problem that the traditional hyperspectral image classification method cannot iden...

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