The Study On Multispectral And Hyperspectral Image Data Fusion And Classification Technology

As a vital part of modern imaging system,multi-spectral images and hyper-spectral images provided abundant spatial information and spectral information for ground object detection and has deep application value in civil and military fields.The processing technologies of multispectral and hyperspectral image includes target detection,image enhancement,image fusion semantic segmentation,anomaly detection and image classification,etc.In this paper,we mainly studied the image fusion and image classification technologies.The main works of this paper is as follows:(1)In this paper,we proposed an RLNSST decomposition and GF based weighted average fusion algorithm.Based on the well understanding of image fusion technology and theory,this paper analyzed the multi-scale analysis fusion method.We combined the RLNSW and the NSST because its outstanding multi-scale decomposition and shift-invariance.Then we use the guided filter because the computation efficiency of linear computation.By combining the above methods,we proposed a fast-multi-scale decomposition weighted average image fusion framework and designed the verification experiment with TNO multispectral image dataset.(2)In this paper,we proposed a convolutional neural network based hyperspectral image classification model.We first analyzed the general difficulties in hyperspectral image classification technology and studied the relevant technology to solve these problems.Firstly,by combining the 3D CNN and residual network,we solved the“gradient vanishing problem” caused by the deep network structure;Secondly,we constructed a weight adjustment module based on visual attention mechanism.By combining the above two methods,we proposed a convolutional neural network based hyperspectral image classification model.The verification experiments are carried on hyperspectral remote sensing datasets and the experimental results verified the validity of the proposed method.