Multi-spectral image classification method based on threshold self-adaption and convolutional neural network

Abstract

The invention discloses a multi-spectral image classification method based on threshold self-adaption and a convolutional neural network. The method comprises the following steps of inputting multispectral images of different time phases and different wave bands of satellites to be classified, and carrying out normalization on all pixels of a marked portion of a same wave band image in all cities; stacking selected 9 wave bands into one image and taking the image as a training data set; constructing a classification model based on the convolutional neural network, using the training data set to train the classification model so as to acquire a probability model based on OSM, using the model and a confidence coefficient strategy to adjust a softmax output result and acquiring a final classification model, and finally, uploading a test result to an IEEE website so as to acquire classification accuracy. By using the multi-spectral image classification method, characteristics that there are a lot of wave bands, a data volume is large and there are a lot of information redundancy in the multispectral images are fully used so that a problem that surface features of complex types are difficult to classify is solved, classification accuracy is increased, an error dividing rate is reduced and a classification speed can be increased.

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

Images