CNN training and remote sensing image target recognition method based on wavelet transform

Abstract

The invention discloses CNN training and remote sensing image target recognition based on wavelet transform, and the method comprises the steps: constructing a training set of a target object; performing image preprocessing on training images in the training set to obtain preprocessed training images; carrying out the wavelet transformation on the preprocessed training image to obtain a transformed wavelet image; and respectively inputting the transformed wavelet image and the preprocessed training image into a convolutional neural network for training. According to the technical scheme of theinvention, the accuracy and recognition precision of the image recognition method based on the convolutional neural network can be improved.

Description

Technical field [0001] The embodiments of the present invention relate to the field of image processing technology, and in particular to a method, device, equipment and medium for CNN training based on wavelet transform and remote sensing image recognition based on wavelet transform convolutional neural network CNN. Background technique [0002] Image recognition is an important field of artificial intelligence. The process of image recognition technology is mainly divided into steps such as information acquisition, preprocessing, feature extraction and selection, classifier design and classification decision-making. [0003] The existing image recognition methods mainly include the image recognition method based on feature extraction and the convolutional neural network (Convolutional Neural Network, CNN) method. Among them, the image recognition methods based on feature extraction mainly include Histogram Of Gradient (HOG), Scale-invariant feature transform (SIFT), and Local Bina...

AI Technical Summary

Problems solved by technology

[0004] In the process of implementing the present invention, the inventor found that the existing technology has the following defects: the features extracted by the image recognition method based on feature extraction only contain shallow information of the image, not high-level semantic information

However, due to the high cost of data labeling, the training samples are limited, and it is easy to cause poor training effect or over-fitting problems.

On the other hand, the feature learning process of the convolutional neural network is based on shallow information, and some important information will inevitably be lost during the convolution process, such as the edge and contour features of the recognition target, resulting in low recognition accuracy.

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