Spectral‐spatial sequence characteristics‐based convolutional transformer for hyperspectral change detection  

作　　者：Chengle Zhou Qian Shi Da He Bing Tu Haoyang Li Antonio Plaza 

机构地区：[1]School of Geography and Planning,Sun Yat‐sen University,Guangzhou,China [2]Guangdong Provincial Key Laboratory for Urbanization and GeoSimulation,Sun Yat‐sen University,Guangzhou,China [3]Institute of Optics and Electronics,Nanjing University of Information Science and Technology,Nanjing,China [4]Hyperspectral Computing Laboratory,Escuela Politecnica,University of Extremadura,Caceres,Spain

出　　处：《CAAI Transactions on Intelligence Technology》2023年第4期1237-1257,共21页智能技术学报（英文）

基　　金：supported in part by by the National Key R&D Program of China under Grant 2022YFB3903402;in part by the National Natural Science Foundation of China under Grant 42222106;in part by the National Natural Science Foundation of China under Grant 61976234 and 42201340。

摘　　要：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.

关 键 词：change detection IMAGEANALYSIS 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]