Research On Semantic Segmentation Algorithm Of Natural Disaster Remote Sensing Image Based On Deep Learning

Semantic segmentation of natural disaster remote sensing images is an important application field of remote sensing technology.It uses computer vision technology to analyze the semantics of remote sensing images and classify pixels into different categories to help researchers better understand the impact of natural disasters on the surface.With the continuous development of remote sensing technology,remote sensing data is more and more widely used in various fields.Especially after the occurrence of natural disasters,the assessment and rescue of the affected areas need a lot of information support.Remote sensing image semantic segmentation technology has become a very important tool.Semantic segmentation of remote sensing image is an important research direction in the field of computer vision.It plays an important role in the application of remote sensing data.However,there are still some problems in the semantic segmentation of remote sensing images,mainly including the following aspects : First,noise and interference.Remote sensing images usually contain a lot of noise and interference,such as clouds,shadows,reflective surfaces,etc.,which will affect the results of semantic segmentation.Then there is insufficient label data.Semantic segmentation requires a lot of annotation data,and the annotation work is very time-consuming and laborious.Finally,the data structure and feature expression between different remote sensing image types are usually different,so how to realize cross-domain semantic segmentation is an important issue.In view of the above problems,this paper studies and improves the algorithm from different angles to improve the application level of remote sensing data.At the same time,it is necessary to strengthen the feature extraction and preprocessing of remote sensing image data to reduce the influence of noise and interference on semantic segmentation results.The main research contents are as follows :Aiming at the problem of inaccurate semantic segmentation edge of high-resolution remote sensing images and inconsistent prediction of disaster-affected areas by convolutional neural networks,a high-resolution remote sensing image segmentation method based on Generative Adversarial Network(GAN)is used for feature extraction of disaster-affected areas.At the same time,compared with the unreliable scene of the traditional adversarial network model,this paper proposes a semi-supervised semantic segmentation model based on residual structure.A branch network is added to the encoder,and unlabeled data is used to train the branch network.The output of the branch network is used as an additional loss of the encoder to constrain the feature extraction of the encoder,thereby improving the generalization ability and robustness of the model.In view of the fact that high-altitude imaging of remote sensing images is susceptible to natural environments such as weather,illumination,and haze,and at the same time reduces the interference of object noise in the image target image,based on the improved PSPNet(Pyramid Scene Analysis Network)model,by introducing a multi-channel domain attention module,a MCA-PSPNet network model structure is proposed by combining maximum pooling and average pooling,which improves the accuracy of ground object boundary segmentation of high-resolution remote sensing images,thus ensuring that the network pays more attention to image segmentation boundary features during training.