Object-Based Superresolution Land-Cover Mapping From Remotely Sensed Imagery
Object-Based Superresolution Land-Cover Mapping From Remotely Sensed Imagery
复制标题
基于遥感图像的基于对象的超分辨率土地覆盖绘图
DOI：
10.1109/tgrs.2017.2747624
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发表时间：
2018-01-01
影响因子：
8.2
通讯作者：
Chen, Yu
中科院分区：
文献类型：
--
作者：
Chen, Yuehong;Ge, Yong;Chen, Yu
超分辨率映射（SRM）是一种广泛使用的技术，以解决混合像素的问题，基于像素的分类。先进的基于对象的分类将面临类似的混合现象，一个混合的对象，包含不同的土地覆盖类。目前，大多数SRM方法集中在基于像素的分类中的混合像素内的类的空间位置估计。几乎没有考虑过预测类在混合对象中的空间分布。因此，本文提出了一种新的基于对象的SRM策略（OSRM）来处理混合对象的基于对象的分类。首先，它使用反卷积技术来估计半变异函数在目标亚像素尺度上的不规则对象的类比例。然后，应用面到点克里格方法，根据估计的半变异函数和对象的类比例来预测每个对象内的子像素的软类值。最后，在对象级的线性优化模型建立，以确定每个对象内的子像素的最佳类标签。两幅合成图像和一幅真实的遥感图像被用来评估OSRM的性能。实验结果表明，OSRM产生更多的土地覆盖混合对象的细节比传统的基于对象的硬分类和性能优于现有的基于像素的SRM方法。因此，OSRM提供了一个有价值的解决方案，混合对象的基于对象的分类。
Superresolution mapping (SRM) is a widely used technique to address the mixed pixel problem in pixel-based classification. Advanced object-based classification will face a similar mixed phenomenon-a mixed object that contains different land-cover classes. Currently, most SRM approaches focus on estimating the spatial location of classes within mixed pixels in pixel-based classification. Little if any consideration has been given to predicting where classes spatially distribute within mixed objects. This paper, therefore, proposes a new object-based SRM strategy (OSRM) to deal with mixed objects in object-based classification. First, it uses the deconvolution technique to estimate the semivariograms at target subpixel scale from the class proportions of irregular objects. Then, an area-to-point kriging method is applied to predict the soft class values of subpixels within each object according to the estimated semivariograms and the class proportions of objects. Finally, a linear optimization model at object level is built to determine the optimal class labels of subpixels within each object. Two synthetic images and a real remote sensing image were used to evaluate the performance of OSRM. The experimental results demonstrated that OSRM generated more land-cover details within mixed objects than did the traditional object-based hard classification and performed better than an existing pixel-based SRM method. Hence, OSRM provides a valuable solution to mixed objects in object-based classification.