Land Classification Using Remotely Sensed Data: Going Multilabel
Land Classification Using Remotely Sensed Data: Going Multilabel
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DOI：
10.1109/tgrs.2016.2520203
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发表时间：
2016-02
影响因子：
8.2
通讯作者：
Konstantinos Karalas;G. Tsagkatakis;M. Zervakis;P. Tsakalides
中科院分区：
文献类型：
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作者：
Konstantinos Karalas;G. Tsagkatakis;M. Zervakis;P. Tsakalides
由于通过实地研究进行人工注释的成本高且劳动密集型过程，获得最新的高分辨率土地覆盖描述是一项具有挑战性的任务。这项工作为实现这一目标提出了一种全新的方法，即利用遥感卫星图像的扩散，从而能够生成最新的全球尺度土地覆盖图。我们提出了多标签分类，在机器学习中的一个强大的框架，推断所获得的卫星图像和不同类型的表面材料的光谱分布之间的复杂关系的应用。引入一个完全不同的方法相比，无监督的光谱分解，我们采用当代地面收集的数据，从欧洲环境署生成的标签集和多光谱图像的MODIS传感器生成的光谱特征，监督分类框架下。为了验证我们的方法的优点，我们使用几个最先进的多标签学习分类器的结果，并评估其预测性能方面的注释训练样本的数量，以及他们的能力，利用来自邻近地区或不同的时间实例的例子。我们还展示了我们的方法的应用程序的高光谱数据从遥感器的城市土地覆盖估计的纽约市。实验结果表明，所提出的框架可以实现出色的预测精度，即使从有限数量的不同的训练样本，超过国家的最先进的光谱分解方法。
Obtaining an up-to-date high-resolution description of land cover is a challenging task due to the high cost and labor-intensive process of human annotation through field studies. This work introduces a radically novel approach for achieving this goal by exploiting the proliferation of remote sensing satellite imagery, allowing for the up-to-date generation of global-scale land cover maps. We propose the application of multilabel classification, a powerful framework in machine learning, for inferring the complex relationships between the acquired satellite images and the spectral profiles of different types of surface materials. Introducing a drastically different approach compared to unsupervised spectral unmixing, we employ contemporary ground-collected data from the European Environment Agency to generate the label set and multispectral images from the MODIS sensor to generate the spectral features, under a supervised classification framework. To validate the merits of our approach, we present results using several state-of-the-art multilabel learning classifiers and evaluate their predictive performance with respect to the number of annotated training examples, as well as their capability to exploit examples from neighboring regions or different time instances. We also demonstrate the application of our method on hyperspectral data from the Hyperion sensor for the urban land cover estimation of New York City. Experimental results suggest that the proposed framework can achieve excellent prediction accuracy, even from a limited number of diverse training examples, surpassing state-of-the-art spectral unmixing methods.