Multispectral image classification method based on deep integrated residual network

Abstract

The present invention discloses a hyperspectral image classification method based on a deep integrated residual network. Defects are overcome that human selection of weak classifiers and design of anintegration method are complex and time-consuming, the computation process is tedious and a classification result has phenomena of the same object with different spectra characteristics and differentobjects with the same spectra characteristic caused by a semi-supervised training mode in the prior art. The implementation of the method comprises the steps of: (1) inputting multispectral images; (2) performing ground object target removal normalization processing of images of each wave band of each multispectral image; (3) obtaining a multispectral image matrix; (4) obtaining a data set; (5) establishing a deep integrated residual network; (6) training the deep integrated residual network; and (7) performing classification of the test data set. The multispectral image classification methodbased on the deep integrated residual network has complete multispectral image features for learning, is more concise and much clearer in process, allows a classification effect to be more accurate, and can be used for classification of hyperspectral images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a multispectral image classification method based on a deep fusion residual network in the technical field of multispectral image classification. The invention can be used to classify ground objects including water areas, fields, ground objects and the like in multi-spectral images. Background technique [0002] Multispectral image is a kind of remote sensing image, which refers to the image formed by the reflection and transmission of electromagnetic waves in multiple bands, including visible light, infrared, ultraviolet, millimeter wave, X-ray, gamma-ray reflection or transmission image. As the basic research of multispectral images, multispectral image classification has always been an important means of information acquisition for multispectral images. Its main goal is to divide each pixel in the image into different categories according to the spatial geometri...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that it needs to select a variety of weak classifiers for ensemble learning, the selection of weak classifiers and the design of the ensemble method rely on human experience, and the training of multiple classifiers is complex and time-consuming. Time

The disadvantage of this method is that the calculation process of this method is cumbersome, and the semi-supervised clustering method is used, which leads to the phenomenon of different spectra of the same object and the same spectrum of different objects in the classification results, which affects the classification accuracy.

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