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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Semisupervised Classification of Remote Sensing Images With Active Queries
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Semisupervised Classification of Remote Sensing Images With Active Queries

机译：具有主动查询的遥感影像的半监督分类

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摘要

We propose a semiautomatic procedure to generate land cover maps from remote sensing images. The proposed algorithm starts by building a hierarchical clustering tree, and exploits the most coherent pixels with respect to the available class information. For a given amount of labeled pixels, the algorithm returns both classification and confidence maps. Since the quality of the map depends of the number and informativeness of the labeled pixels, active learning methods are used to select the most informative samples to increase confidence in class membership. Experiments on four different data sets, accounting for hyperspectral and multispectral images at different spatial resolutions, confirm the effectiveness of the proposed approach, and how active learning techniques reduce the uncertainty of the classification maps. Specifically, more accurate results with fewer labeled samples are obtained. Inclusion of spatial information in the classifiers drastically improves the classification accuracy, leading to faster convergence curves and tighter confidence intervals. In conclusion, the presented algorithm provides efficient image classification and, at the same time, yields a confidence map that may be very useful in many Earth observation applications.
机译：我们提出了一种半自动程序，可以从遥感图像生成土地覆盖图。所提出的算法从构建分层聚类树开始，并且针对可用的类别信息利用最一致的像素。对于给定数量的标记像素，算法将返回分类图和置信度图。由于地图的质量取决于标记像素的数量和信息性，因此使用主动学习方法来选择信息最丰富的样本，以增加对类成员的信心。在四个不同的数据集上进行的实验（考虑了不同空间分辨率下的高光谱和多光谱图像）证实了该方法的有效性，以及主动学习技术如何减少分类图的不确定性。具体而言，可获得具有较少标记样品的更准确结果。将空间信息包含在分类器中可极大地提高分类精度，从而导致更快的收敛曲线和更紧密的置信区间。总之，提出的算法提供了有效的图像分类，同时产生了置信图，这在许多地球观测应用中可能非常有用。

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