The earth is constantly being changed by natural events and human activities that constantly threaten our environment. Therefore, accurate and timely monitoring of changes at the surface of the earth is of great importance for properly facing their consequences. This research presents a new hyperspectral change detection (HCD) framework based on a robust binary mask and convolutional neural network (CNN). The proposed method is implemented in three parts: (1) the first part provides a robust binary change map based on Otsu and dynamic time wrapping (DTW) algorithms; the DTW algorithm plays a predictor role that is a robust predictor for HCD purposes. Also, Otsu’s algorithm gives an estimate about the approximate threshold for detecting change and no-change class areas. These class areas will be used in the next steps. (2) The second part generates pseudo training data based on an image differencing (ID) algorithm and spectral unmixing (SU) manner for multiple change detection. This pseudo training data will be used for training the CNN model in the next step. (3) Finally, the multiple change map is generated by training the CNN network based on pseudo training data. The result of HCD maps is compared to other robust hyperspectral change detection methods by two real bi-temporal hyperspectral image datasets. The result of HCD in multiple change map shows the proposed method can have high performance compared to other HCD methods with an overall accuracy (OA) of more than 92% and Kappa coefficient (KC) of 0.77 and higher.

自然事件和人类活动不断威胁着我们的环境，不断地改变着地球。因此，准确及时地监测地球表面的变化对于正确应对其后果具有重要意义。本研究提出了一种新的高光谱变化检测 (HCD) 框架，该框架基于稳健的二进制掩码和卷积神经网络 (CNN)。所提出的方法分三部分实现：（1）第一部分提供了基于 Otsu 和动态时间包装（DTW）算法的鲁棒二进制变化图；DTW 算法起着预测器的作用，它是用于 HCD 目的的稳健预测器。此外，Otsu 算法给出了检测变化和不变的近似阈值的估计类区域。这些类区域将在接下来的步骤中使用。(2)第二部分基于图像差分(ID)算法和光谱分离(SU)方式生成伪训练数据，用于多重变化检测。该伪训练数据将用于下一步训练 CNN 模型。(3) 最后，通过基于伪训练数据训练CNN网络生成多重变化图。通过两个真实的双时相高光谱图像数据集，将 HCD 地图的结果与其他稳健的高光谱变化检测方法进行了比较。多变化图中的 HCD 结果表明，与其他 HCD 方法相比，该方法具有较高的性能，总体准确率（OA）超过 92%，Kappa 系数（KC）为 0.77 及更高。