高光谱作为“图谱合一”的遥感技术，具有精细光谱和空间影像的地面覆被观测与识别优势。然而，高光谱遥感数据的光谱信息表征以及空间信息的利用给双时相高光谱遥感图像变化检测任务带了巨大的挑战。为此，本文探讨了一种光谱-频域属性模式融合的高光谱遥感图像变化检测方法（Spectral-Frequency Domain Attribute Pattern Fusion, SFDAPF）。首先，本文设计了一种基于梯度相关性的光谱绝对距离，使双时相高光谱遥感图像像元对的属性模式从光谱信息表征方面得到了逐级量化。其次，本文基于傅里叶变换理论提出了一种变化像元属性模式显著性增强策略，从全局空间信息利用方面改善了变化与非变化属性像元对的可分性。然后，将全图属性模式显著性水平与梯度相关性的光谱绝对距离进行融合，得到变化检测的综合界定值。最后，依据虚警阈值确定双时相高光谱遥感图像变化检测的二值化结果。本文方法在开源的双时相高光谱遥感图像河流和农场数据集上进行了变化检测性能验证，结果表明：本文SFDAPF方法能够优于传统的和最新的变化检测方法，变化检测的总体精度在河流和农场数据集上分别达到了0.96508和0.97287（最高精度为1.00000），证明了本文方法的有效性。

Hyperspectral imagery (HSI) is a three-dimensional cube data that combines spatial imagery and spectral information, which brings many conveniences to the accurate interpretation of observation information of ground coverings. However, there are also challenges in high-dimensional nonlinear data processing for the HSI change detection (HSI-CD) task. Therefore, a HSI change detection method based on spectral-frequency domain attribute pattern fusion (SFDAPF) is introduced to quantify the spectral representation of pixel attribute patterns step by step. Specifically, a saliency enhancement (SE) strategy for pixel attribute patterns based on Fourier transform theory is developed to improve the separability between pixel attribute patterns in our work. The proposed SFDAPF method consists of four components as follows. First, a gradient correlation-based spectral absolute distance (GCASD) is designed in this paper, so that the attribute patterns of pixel pairs in bitemporal HSI can be quantified step by step from the aspect of spectral information representation. Then, according to Fourier transform theory, this paper proposed a SE strategy of attribute patterns of pixel pairs, which improves the separability of attribute patterns of changing and non-changing pixel pairs in terms of global spatial information utilization. Next, the saliency level and GCASD per pixel are fused to obtain the comprehensive discrimination value of change detection. Finally, according to the false alarm threshold, the binarization results of the bitemporal HSI-CD are obtained. The proposed SFDAPF method was applied to two open-source bitemporal HSI datasets, i.e., River and Farmland datasets. Experimental results show that the proposed SFDAPF method can outperform the traditional and state-of-the-art HSI-CD methods. Specifically, the overall accuracy of HSI-CD achieves 0.96508 and 0.97287 on the river and farmland datasets, respectively. The experiments verified the effectiveness of the proposed SFDAPF method in the HSI-CD task. In general, the proposed SFDAPF method fully considers the representation of spectral information and the utilization of neighborhood spatial information, and thus promoting overall accuracy of HSI-CD. However, the proposed SFDAPF method only considers the single-window eight-connected neighborhood in the spectral characterization stage as well as the magnitude features represented in the frequency domain. Therefore, in future research work, we will further explore the contribution of dual-window spectral information representation and phase information of frequency domain representation to HSI-CD task.