Recently,ground coverings change detection(CD)driven by bitemporal hyperspectral images(HSIs)has become a hot topic in the remote sensing community.There are two challenges in the HSI‐CD task:(1)attribute feature rep...Recently,ground coverings change detection(CD)driven by bitemporal hyperspectral images(HSIs)has become a hot topic in the remote sensing community.There are two challenges in the HSI‐CD task:(1)attribute feature representation of pixel pairs and(2)feature extraction of attribute patterns of pixel pairs.To solve the above problems,a novel spectral‐spatial sequence characteristics‐based convolutional transformer(S3C‐CT)method is proposed for the HSI‐CD task.In the designed method,firstly,an eigenvalue extrema‐based band selection strategy is introduced to pick up spectral information with salient attribute patterns.Then,a 3D tensor with spectral‐spatial sequence characteristics is proposed to represent the attribute features of pixel pairs in the bitemporal HSIs.Next,a fusion framework of the convolutional neural network(CNN)and Transformer encoder(TE)is designed to extract high‐order sequence semantic features,taking into account both local context information and global sequence dependencies.Specifically,a spatial‐spectral attention mechanism is employed to prevent information reduction and enhance dimensional interactivity between the CNN and TE.Finally,the binary change map is determined according to the fully‐connected layer.Experimental results on real HSI datasets indicated that the proposed S3C‐CT method outperforms other well‐known and state‐of‐the‐art detection approaches in terms of detection performance.展开更多
基金supported in part by by the National Key R&D Program of China under Grant 2022YFB3903402in part by the National Natural Science Foundation of China under Grant 42222106in part by the National Natural Science Foundation of China under Grant 61976234 and 42201340。
文摘Recently,ground coverings change detection(CD)driven by bitemporal hyperspectral images(HSIs)has become a hot topic in the remote sensing community.There are two challenges in the HSI‐CD task:(1)attribute feature representation of pixel pairs and(2)feature extraction of attribute patterns of pixel pairs.To solve the above problems,a novel spectral‐spatial sequence characteristics‐based convolutional transformer(S3C‐CT)method is proposed for the HSI‐CD task.In the designed method,firstly,an eigenvalue extrema‐based band selection strategy is introduced to pick up spectral information with salient attribute patterns.Then,a 3D tensor with spectral‐spatial sequence characteristics is proposed to represent the attribute features of pixel pairs in the bitemporal HSIs.Next,a fusion framework of the convolutional neural network(CNN)and Transformer encoder(TE)is designed to extract high‐order sequence semantic features,taking into account both local context information and global sequence dependencies.Specifically,a spatial‐spectral attention mechanism is employed to prevent information reduction and enhance dimensional interactivity between the CNN and TE.Finally,the binary change map is determined according to the fully‐connected layer.Experimental results on real HSI datasets indicated that the proposed S3C‐CT method outperforms other well‐known and state‐of‐the‐art detection approaches in terms of detection performance.
