INTERACTIVE CHANGE DETECTION USING HIGH RESOLUTION REMOTE SENSING IMAGES BASED ON ACTIVE LEARNING WITH GAUSSIAN PROCESSES
INTERACTIVE CHANGE DETECTION USING HIGH RESOLUTION REMOTE SENSING IMAGES BASED ON ACTIVE LEARNING WITH GAUSSIAN PROCESSES
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DOI：
10.5194/isprs-annals-iii-7-141-2016
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Hui Ru;Huai Yu;Pingping Huang;Wen Yang
中科院分区：
文献类型：
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作者：
Hui Ru;Huai Yu;Pingping Huang;Wen Yang
抽象的。尽管对于变化检测已经有很多研究，但高分辨率遥感图像的有效和高效利用仍然是一个问题。传统的监督方法需要大量注释来对土地覆盖类别进行分类并检测其变化。此外，监督方法中的训练集通常有大量冗余样本，没有任何基本信息。在本研究中，我们提出了一种利用高分辨率遥感图像进行主动学习的交互式变化检测方法，以克服现有遥感图像变化检测技术的不足。在我们的方法中，一开始没有实际土地覆盖类别的注释。首先，我们以无监督的方式找到一定数量的最具代表性的对象。然后，通过高斯过程的主动学习，以交互的方式逐渐检测多时相高分辨率遥感图像的变化区域，直到检测结果不再发生明显变化。可以大大减少人工标记，并且只需几次迭代即可获得理想的检测结果。 Geo-Eye1和WorldView2遥感图像的实验证明了我们提出的方法的有效性和效率。
Abstract. Although there have been many studies for change detection, the effective and efficient use of high resolution remote sensing images is still a problem. Conventional supervised methods need lots of annotations to classify the land cover categories and detect their changes. Besides, the training set in supervised methods often has lots of redundant samples without any essential information. In this study, we present a method for interactive change detection using high resolution remote sensing images with active learning to overcome the shortages of existing remote sensing image change detection techniques. In our method, there is no annotation of actual land cover category at the beginning. First, we find a certain number of the most representative objects in unsupervised way. Then, we can detect the change areas from multi-temporal high resolution remote sensing images by active learning with Gaussian processes in an interactive way gradually until the detection results do not change notably. The artificial labelling can be reduced substantially, and a desirable detection result can be obtained in a few iterations. The experiments on Geo-Eye1 and WorldView2 remote sensing images demonstrate the effectiveness and efficiency of our proposed method.