Full convolution network based remote-sensing image land cover classifying method

Abstract

The invention relates to a full convolution network based remote-sensing image land cover classifying method. The method comprises the following steps: S1, performing data enhancement on a data set with limited data quantity; and generating a training set of which the data quantity and quality meet the training requirement; S2, combining the improved full convolution network FCN4s and the improvedU type full convolution network U-NetBN; and building a remote-sensing image land cover classifying model; S4, minimizing the cross entropy loss as the decrease of random gradient; learning the optimal parameters of the model to obtain the trained remote-sensing image land cover classifying model; and S4, performing pixel class classifying prediction on the predicted remote-sensing image throughthe trained remote image land cover classifying model. According to the method, the properties of the FCN full convolution network and the U-Net full convolution network are combined, so that the remote-sensing image land cover classifying performance can be improved.

Description

technical field [0001] The invention relates to the fields of image processing and computer vision, in particular to a method for classifying land cover of remote sensing images based on a fully convolutional network. Background technique [0002] With the continuous improvement of the resolution of satellite remote sensing images and aerial remote sensing images, people can obtain more useful data and information from remote sensing images. Land cover classification of remote sensing images is an important content in the field of remote sensing research. It has strong application value in various fields such as land, national defense, surveying and mapping, agriculture, cities, and disaster prevention and reduction. Therefore, it is very important to improve the accuracy of land cover classification of remote sensing images. Significance. The first surface classification method of remote sensing images is the image visual interpretation technology, that is, the image is ma...

AI Technical Summary

Problems solved by technology

[0003] At present, there are few related studies on the land cover classification of remote sensing images at the pixel level. Traditional machine learning-based classification methods, in order to classify a pixel, need to use an image block around the pixel as input for training and It is predicted that adjacent pixel blocks are basically repeated, and the calculations for each pixel block are also repeated to a large extent. The storage overhead is large and the calculation efficiency is low. It is necessary to find a more accurate and efficient pixel-level remote sensing. Satellite land cover classification method to meet the needs of existing applications

Images