Hyperspectral (HS) images captured from Earth by satellite and aircraft have become increasingly important in several environmental and ecological contexts (e.g. agriculture and urban areas). In the present study we propose an iterative learning methodology for the change detection of HS scenes taken at different times in the same areas. It cascades clustering and classification through iterative learning, in order to separate salient regions, where a change occurs in the scene from the unchanged background. The iterative learning is evaluated in both the clustering and the classification steps. The experiments performed with the proposed methodology provide encouraging results, also compared to several recent state-of-the-art competitors.

在若干环境和生态环境（例如农业和城市地区）中，通过卫星和飞机从地球捕获的高光谱（HS）图像变得越来越重要。在本研究中，我们提出了一种迭代学习方法，用于在相同区域的不同时间拍摄的HS场景的变化检测。它通过迭代学习将聚类和分类进行级联，以分离显着区域，在该显着区域中，场景发生了变化，背景保持不变。在聚类和分类步骤中都评估了迭代学习。与最近的几个最先进的竞争对手相比，使用所提出的方法进行的实验也提供了令人鼓舞的结果。