Hyperspectral open set classification method based on Euclidean distance and deep learning

Abstract

The invention belongs to the technical field of image processing, and discloses a hyperspectral open set classification method based on Euclidean distance and deep learning. The method comprises the following steps: firstly, constructing a category center of each known category, a category prediction function based on an Euclidean distance and a loss function based on the Euclidean distance, and training and optimizing a deep learning network model; secondly, combining a box line graph method and an extreme value theory Weibull distribution model, analyzing and fitting the distance between input data and the category center of each known category, thereby achieving constraint on each known classification boundary, and then achieving judgment on unknown categories. Specifically, a box linegraph is used for providing the number of abnormal points needed for fitting the Weibull distribution model; and judging whether a Weibull model is used or the upper edge of the box line graph is usedfor judging unknown classes according to the number of the abnormal points. Aiming at the open set classification problem in the hyperspectral field, the method is simple, practical, high in classification precision and high in robustness.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a hyperspectral open set classification method based on Euclidean distance and deep learning. In the testing process, the invention can not only accurately classify the known classes appearing in the training process, but also identify and reject the unknown classes not appearing in the training process. Background technique [0002] Hyperspectral classification technology is a technology that uses spectral information and spatial information to classify each pixel in hyperspectral images, and has great application value in national defense and civilian fields. In recent years, with the rise of deep learning technology, classification models based on convolutional neural networks and their derivative models (such as residual networks, recurrent networks, and densely connected networks) have gradually become important methods in the field of hyperspectral classification. ...

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Problems solved by technology

Therefore, in the actual use process, the robustness and generalization of comparison file 2 and comparison file 3 are poor, and the performance of unknown target detection fluctuates greatly

[0006] To sum up, the challenge of hyperspectral open set classification tas

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