The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method.

由于人类和自然因素，地球的环境正在不断变化。在几个应用领域中，及时确定位置和变化种类至关重要。因此，遥感变化检测是一个非常令人感兴趣的话题。精确的变化检测方法的发展一直是一个挑战。本研究介绍了一种基于数据聚类和优化的新型无监督变更检测方法。与传统方法相比，该提案较少依赖于辐射归一化。我们使用遥感图像和模拟数据集进行了实验，以将所提出的方法与其他无监督的已知技术进行比较。在最好的情况下，该建议将次优技术的准确性提高了50％。对于未经校准的数据，这种改进最为明显。通过模拟数据进行的实验表明，该建议在任何实际意义上均优于所有其他比较方法。结果表明了该方法的潜力。