Semantic segmentation of remote sensing image based on U-NET

Abstract

At present, the image processing of remote sensing technology mainly depends on the transcendental ability of human beings, and it needs to spend a lot of artificial resources to mark. Therefore, this paper proposes a research and application of semantic segmentation method for remote sensing images based on convolutional neural network. Normalize the data, subtract the mean value and divide it by the standard deviation to standardize, divide the data, introduce data enhancement to further enhance the training data, and create a convolutional neural network and a training network. Each layer of U-NET is composed of three layers of convolution, and features are extracted and integrated by pooling or up-sampling. At the last layer, all the previously extracted features are classified into two categories to realize the semantic segmentation of the image. The experimental results show that the F1 score, Recall score and Precision score of this method are 84.31%, 89.59% and 79.62%, respectively. By introducing U-NET, the semantic segmentation accuracy of remote sensing images is improved. Compared with the traditional full convolution neural network, U-NET has been improved. Through the stronger connection between layers, plus up-sampling and down-convolution, features can be fully extracted and accurate segmentation can be achieved with fewer training samples.