Multispectral Image Classification Method Based on Adaptive Threshold and Convolutional Neural Network

Abstract

The invention discloses a multi-spectral image classification method based on threshold self-adaptation and convolutional neural network, which inputs multi-spectral images of satellites to be classified in different time phases and different bands, and converts the marked parts of the same band images of all cities All pixels are normalized; the selected 9 bands are stacked into an image as a training data set; a classification model based on convolutional neural network is constructed, and the training data set is used to train the classification model to obtain a probability model based on OSM. The model and the confidence strategy adjust the softmax output results to obtain the final classification model, and finally upload the test results to the IEEE website to obtain the classification accuracy. The multi-spectral image classification method provided by the present invention makes full use of the characteristics of multi-spectral image with multiple bands, large data volume, and large information redundancy, and solves the problem that it is difficult to classify complex types of ground objects, and can not only improve the classification accuracy , Reduce the misclassification rate, and can also improve the classification speed.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a multi-source, multi-temporal, multi-mode multispectral image classification method based on threshold self-adaptation and convolutional neural network. Background technique [0002] Multispectral images refer to the images formed by the reflection and transmission of electromagnetic waves of any band by objects, including visible light, infrared rays, ultraviolet rays, millimeter waves, X-rays, and gamma-ray reflection or transmission images. Multispectral image fusion refers to combining the multispectral image information features of the same scene obtained from multispectral detectors, and using their temporal and spatial correlation and information complementarity to obtain a more comprehensive and clear description of the scene. . For example, infrared images and visible light images are complementary: to the human eye, visible light has rich details ...

AI Technical Summary

Problems solved by technology

[0006] However, none of the above classification methods take into account that the multispectral data has fewer bands and low spectral resolution. Not only is the data volume large, but it is also difficult to classify complex types of ground objects. Therefore, it is difficult to obtain high classification accuracy

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