Study on the example segmentation method of remote sensing image based on neural network

Abstract

With the rapid development of modern science and technology, people collect remote sensing data from the altitude perspective at the same time, put forward the use of a variety of remote sensing images to solve the military exploration, meteorological analysis, environmental protection, resource exploration and other basic problems. However, since remote sensing images have the characteristics of too large data, high image resolution and extremely low application efficiency, some scholars have used the image features in residual network problems in their research to solve the problem of remote sensing image target segmentation scale difference based on the attention mechanism and single-step case segmentation framework. In this paper, based on the understanding of the research status of neural networks and remote sensing image application, a remote sensing image segmentation model based on multi-level channel attention is proposed according to the model architecture of convolutional neural networks. The final experimental results show that the neural network based remote sensing image case segmentation technology has positive effects.