Remote sensing image classification method based on active learning and convolutional neural network
A convolutional neural network and active learning technology, applied in the field of hyperspectral remote sensing image classification, can solve the problems of expensive, time-consuming and labor-intensive label samples, and a large number of other problems
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[0021] The technical solutions in the embodiments of the present invention will be described clearly and in detail below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention.
[0022] See figure 1 , The specific steps of the present invention are:
[0023] (1) Obtain hyperspectral remote sensing data as required;
[0024] (2) Principal component analysis of hyperspectral remote sensing data and processing it into data blocks;
[0025] (3) Divide the data into training set, unlabeled sample set, validation set and test set according to a certain proportion;
[0026] (4) Input the training samples into the convolutional neural network for training, and predict the category of the samples in the unlabeled sample set;
[0027] (5) Use active learning to evaluate the samples in the unlabeled sample set, sort the confidence of the samples, and select the samples with high confidence ...
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