Multispectral Image Classification Method Based on Recoding and Deep Fusion Convolutional Networks

Abstract

The invention discloses a multi-spectral image classification method based on data recoding and deep fusion convolutional network, which stacks data of different bands in multi-spectral images to be classified to obtain three-dimensional image features FA; input the multi-spectral image to be classified Classify auxiliary data; then encode to obtain the three-dimensional image feature FB; combine the three-dimensional feature matrix FA and FB obtained after processing the two data sources as the input feature F; normalize F, and normalize the normalized feature Each element in the matrix F1 takes a block to form a feature matrix F2 based on the image block; according to F2, the feature matrix W1 of the training data set and the feature matrix W2 of the test data set are obtained; a classification model based on a multi-scale depth filter is constructed; The feature matrix W1 of the training data set is used to train the classification model; the feature matrix W2 of the test data set is used to classify the trained classification model. The invention improves the classification accuracy of the multi-spectral remote sensing images and can be used for classification of ground objects.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a multispectral image classification method based on data recoding and deep fusion convolutional network. Background technique [0002] Multi-spectral images are remote sensing images obtained by using satellite multi-spectral ground scanning systems, usually containing more than two spectral channels for synchronous imaging of ground objects. [0003] With the rapid development of the economy, the land use is also constantly changing. It is also becoming more and more difficult to monitor land use in real time by human. The development of satellite remote sensing technology has solved this problem to a certain extent. Through high-altitude scanning and imaging, satellite remote sensing can obtain real-time information on ground conditions, and has been more and more widely used in land use monitoring and other aspects. As an important source of remote sensing data,...

AI Technical Summary

Problems solved by technology

[0006] However, in the processing of multispectral remote sensing images, the traditional convolutional neural network only extracts features on one scale and performs feature re-extraction for the same target. It does not consider the information fusion problem of different scales and cannot make full use of existing It is difficult to achieve high classification accuracy due to the data information

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