Change detection (CD) is a hot issue in the research of remote sensing technology. Hyperspectral images (HSIs) greatly promote the development of CD technology because of their high resolution in the spectral domain. However, some traditional CD methods currently applied to low-dimensional and multispectral images cannot adapt to the complex high-dimensional features of the HSIs. In addition, the spectral measurements of the HSI contain a lot of noise and redundancy, which greatly contaminates spectral-only information for CD. In order to fully extract the discriminant features of HSI to improve the accuracy of CD, this article proposes a three-directions spectral–spatial convolution neural network (TDSSC). A novel method for three-direction decomposition of hyperspectral change tensors is proposed—change tensor is decomposed along the spectral direction and two spatial directions to get a single tensor containing the spectral information and two kinds of tensors containing the spectral–spatial information. TDSSC uses 1-D convolution to extract spectral features from the spectral direction as well as reducing the tensor dimension, which helps the latter network to be lightweight and significantly improves the speed of change detection. Also, it uses 2-D convolution to extract spectral–spatial features from two spatial directions of the reduced tensor, and to extract features from different directions to improve the accuracy and Kappa value of CD. The experimental results of three real hyperspectral datasets show that TDSSC is superior to most existing CD methods.

中文翻译：

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TDSSC：用于高光谱图像变化检测的三向光谱空间卷积神经网络

变化检测（CD）是遥感技术研究的热点问题。高光谱图像 (HSI) 因其在光谱域中的高分辨率而极大地促进了 CD 技术的发展。然而，目前应用于低维和多光谱图像的一些传统 CD 方法无法适应 HSI 复杂的高维特征。此外，HSI 的频谱测量包含大量噪声和冗余，极大地污染了 CD 的纯频谱信息。为了充分提取HSI的判别特征以提高CD的准确性，本文提出了一种三向谱空间卷积神经网络（TDSSC）。提出了一种新的高光谱变化张量三向分解方法——将变化张量沿光谱方向和两个空间方向分解，得到包含光谱信息的单个张量和包含光谱-空间信息的两种张量。TDSSC使用一维卷积从光谱方向提取光谱特征并降低张量维数，有助于后者网络轻量化，显着提高变化检测速度。此外，它使用二维卷积从缩减张量的两个空间方向提取光谱-空间特征，并从不同方向提取特征，以提高 CD 的准确性和 Kappa 值。