Remote sensing image classification method based on active learning and convolutional neural network

Abstract

The invention requests to protect a remote sensing image classification method based on active learning and a convolutional neural network, and the method comprises: carrying out the waveband processing of a hyperspectral remote sensing image through a principal component analysis method, and then processing the image into blocks; dividing the data into a training set, an unmarked sample set, a verification set and a test set according to a certain proportion; and training the convolutional neural network by using the training set, predicting the category to which the sample in the unmarked sample set belongs, and introducing active learning to evaluate the sample; and then sorting the evaluation results, selecting samples with low confidence, assigning labels to the samples by experts, and automatically assigning labels to the samples with high confidence by a computer. A high-quality training sample set is constructed by adjusting a prediction label coefficient, and a classifier model is iteratively optimized by using the selected training sample set. And stopping iteration when a stop condition is met, and outputting a final classification result.

Description

technical field [0001] The invention belongs to the field of remote sensing image classification. It specifically involves a convolutional neural network, which introduces active learning to select samples with low confidence, experts assign labels to them, selects samples with high confidence, and the computer automatically assigns them labels, and constructs high-quality samples by adjusting the predicted label coefficients. The sample set, and further use the classifier model to classify hyperspectral remote sensing images. Background technique [0002] Remote sensing image classification is a popular research content in remote sensing technology at present. Remote sensing image classification is to judge each pixel in the image as the object category it belongs to. Therefore, it is of great value to study remote sensing image classification technology. Remote sensing images are widely used in agriculture, environmental monitoring, military and other fields. How to clas...

AI Technical Summary

Problems solved by technology

However, the convolutional neural network is a supervised classification method that requires a large number of labeled samples to achieve high classification accuracy, and the acquisition of labeled samples is not only time-consuming but also very expensive.

At present, the application of convolutional neural network to remote sensing image classification only focuses on randomly initializing the training set for model training, and few scholars consider building a high-quality training set.

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