Detection of volcanic disaster scene from high-resolution remote sensing image with deep learning
In the existing intelligent detection of volcanic disaster scenes from high-resolution remote sensing images,the diverse types of ground objects and the missing sample labels in volcanic disaster scenes are of critical importance,a detection method based on deep learning for volcanic disaster scene from high-resolution remote sensing image is proposed in this paper.Firstly,based on multi-instance learning(MIL)framework,we use joint pyramid upsampling(JPU)to replace dilated convolution module.Then,using the prototype learning and attention mechanism to reconstruct deep neural network model of volcanic disaster scene feature representation simultaneously.Finally,the xBD dataset is used to test the constructed model performance.The experimental results show that compared with the convolutional neural network(CNN),MIL method,and"CNN+"deep learning methods,our proposed method can achieve the minimum standard deviation and the highest precision and detection accuracy without significantly increasing computational time,and has good visual effects.In addition,we further use the proposed method to detect the Hunga Tonga-Hunga Ha'apai(HTHH)volcanic disaster scenes from multi-source and multi temporal high-resolution remote sensing images on January 14th and 15th,2022,and the detection results have good consistency with existing researches.