Convolutional neural networks (CNNs) with excellent spatial feature extraction abilities have become popular in remote sensing (RS) image change detection (CD). However, CNNs often focus on the extraction of spatial information but ignore important spectral and temporal sequences for hyperspectral images (HSIs). In this article, we propose a joint spectral, spatial, and temporal transformer for hyperspectral image change detection (HSI-CD), named SST-Former. First, the SST-Former position-encodes each pixel on the cube to remember the spectral and spatial sequences. Second, a spectral transformer encoder structure is used to extract spectral sequence information. Then, a class token for storing the class information of a single temporal HSI concatenates the output of the spectral transformer encoder. The spatial transformer encoder is used to extract spatial texture information in the next step. Finally, the features of different temporal HSIs are sent as the input of temporal transformer, which is used to extract useful CD features between the current HSI pairs and obtain the binary CD result through multilayer perceptron (MLP). We evaluate the SST-Former on three HSI-CD datasets by numerous experiments, showing that it performs better than other excellent methods both visually and qualitatively. The codes of this work will be available at https://github.com/yanhengwang-heu/IEEE_TGRS_SSTFormer.

中文翻译：

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用于高光谱图像变化检测的光谱“空间”时间变换器


具有出色空间特征提取能力的卷积神经网络（CNN）已在遥感（RS）图像变化检测（CD）中变得流行。然而，CNN 通常专注于空间信息的提取，而忽略了高光谱图像 (HSI) 的重要光谱和时间序列。在本文中，我们提出了一种用于高光谱图像变化检测（HSI-CD）的联合光谱、空间和时间变换器，名为 SST-Former。首先，SST-Former 对立方体上的每个像素进行位置编码以记住光谱和空间序列。其次，使用频谱变换器编码器结构来提取频谱序列信息。然后，用于存储单个时间HSI的类别信息的类别令牌连接频谱变换器编码器的输出。空间变换编码器用于下一步提取空间纹理信息。最后，不同时间HSI的特征作为时间变换器的输入，用于提取当前HSI对之间有用的CD特征，并通过多层感知器（MLP）获得二进制CD结果。我们通过大量实验在三个 HSI-CD 数据集上评估 SST-Former，表明它在视觉和质量上都比其他优秀方法表现得更好。这项工作的代码可在 https://github.com/yanhengwang-heu/IEEE_TGRS_SSTFormer 获取。