Hyperspectral images classification based on pseudo-siamese networks
Hyperspectral image classification based on deep learning architecture has been one of the research hotspots in remote sensing in recent years.However,it is still a challenging problem how to develop new classification frameworks to effectively classify hyperspectral data with a small number of labeled samples.To address this issue,this paper designs an improved pseudo-siamese network for hyperspectral image spectral-spatial classification.The method first divides a high-dimensional hyper-spectral image into two low-dimensional images,and adopts convolutional neural network and graph convolutional network for feature extraction,respectively.The extracted spectral information is then integrated through cascade operation.Finally,the post-cascade features are input to the fully con-nected neural network for classification.The proposed method improves the classical pseudo-siamese network and applies it to hyperspectral image classification.Experimental results and comparative re-sults on two practical hyperspectral datasets verify the effectiveness of the proposed method.
万方数据
维普
