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Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

机译：浮动引用驱动的无监督变化检测：模式分析方法

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摘要

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.
机译：由于人类和自然因素，地球的环境正在不断变化。及时识别所在地的位置和种类在若干应用领域至关重要。因此，遥感变化检测是一个极其兴趣的主题。精确变化检测方法的发展是一个不变的挑战。本研究介绍了一种基于数据聚类和优化的新型无监督变化检测方法。该提议较少依赖于比经典方法的辐射归一化。我们通过遥感图像和模拟数据集进行实验，以将提出的方法与其他无监督的众所周知的技术进行比较。最佳，该提案提高了第二种最佳技术的准确性的50％。这种改进对于未校准数据最引人注目。模拟数据的实验表明，该提案优于任何其他实际意义水平的所有其他比较方法。结果表明了所提出的方法的潜力。

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