ABSTRACT Current research investigations using remotely sensed images are offered with a plethora of sources to explore land cover/land use applicability. Some of the recent advances have shown the advantage of fusing different data sources in land-cover analysis. Though intuitively combined processing of multi-modal imagery should provide better classification of land cover, there are not many work towards this direction and a theoretical framework is not laid out properly. In this work, we are providing such a framework where scattering and spectral properties (from synthetic aperture radar and multi-spectral images, respectively) of ground materials are used to distinguish land-cover classes with higher precision. Different kinds of information that are represented by these two modes of imageries are semantically bridged to infer more distinguishable land-cover classes in an unsupervised framework. The proposed technique is implemented in two phases, i.e., (1) sampling of seed pixels from imageries, and (2) training of representative features and prediction of classes using random forest classifier. Experimental results also show the effectiveness of this fusion of multi-modal image characteristics in classifying the underlying land cover.

中文翻译：

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使用来自多光谱和混合极化 SAR 图像的多模态数据对土地覆盖进行无监督分类

摘要当前使用遥感图像的研究调查提供了大量资源来探索土地覆盖/土地利用的适用性。最近的一些进展显示了在土地覆盖分析中融合不同数据源的优势。虽然多模态影像的直观组合处理应该可以提供更好的土地覆盖分类，但是朝着这个方向的工作并不多，理论框架也没有很好地布局。在这项工作中，我们提供了这样一个框架，其中地面材料的散射和光谱特性（分别来自合成孔径雷达和多光谱图像）用于以更高的精度区分土地覆盖类别。由这两种图像模式表示的不同类型的信息在语义上桥接，以在无监督框架中推断出更可区分的土地覆盖类别。所提出的技术分两个阶段实施，即（1）从图像中采样种子像素，以及（2）使用随机森林分类器训练代表性特征和预测类别。实验结果还显示了这种多模态图像特征融合在对底层土地覆盖进行分类方面的有效性。