Sub-pixel land-cover mapping with improved fraction images upon multiple-point simulation
Sub-pixel land-cover mapping with improved fraction images upon multiple-point simulation
复制标题
基于多点模拟的改进分数图像的亚像素土地覆盖测绘
DOI：
10.1016/j.jag.2012.04.013
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发表时间：
2013-06-01
影响因子：
7.5
通讯作者：
Ge, Yong
中科院分区：
文献类型：
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作者：
Ge, Yong
软分类的输出固有地包含不确定性。作为亚像素映射（SPM）方法的输入，不确定性传播到SPM结果，特别是类之间的边界区域。因此，减少软分类输出中的不确定性是值得探索的。本文首先利用多点模拟（MPS），通过训练图像来表征表面对象/类的空间结构特性。因此，MPS结果用于增加表面对象/类别的分数图像的准确性。改进的分数图像，然后输入到SPM方法产生的土地覆盖图具有更高的空间分辨率。为了验证所提出的方法，在中国千烟洲红土丘陵地区的Landsat TM 30 m的遥感图像。本实验不仅比较了SPM与MPS改进分数图像的分类结果和SPM与原始分数图像的分类结果，而且研究了不同软分类器的性能。实验结果表明，该方法能有效降低软分类结果的不确定性，提高边界区域的识别精度，从而提高SPM模拟图像的精度。(C)2012爱思唯尔有限公司版权所有。
Outputs of soft classification inherently contain uncertainty. As an input for the sub-pixel mapping (SPM) method, the uncertainty is propagated to SPM result especially the boundary region between classes. Therefore, reducing the uncertainty within the outputs of soft classification is worth exploring. This paper firstly utilizes multiple-point simulation (MPS) through training images for characterizing the spatial structural properties of a surface object/class. Consequently, MPS results are used to increase the accuracy of the fraction image of the surface object/class. The improved fraction image then inputs to the SPM method for producing the land cover map with finer spatial resolution. In order to validate the proposed method, a remotely sensed image from Landsat TM 30 m over the Qianyanzhou red earth hill region in China is used. This experimental study not only compares the results from SPM with improved fraction images with MPS and results from SPM with original fraction images, but also investigates the performances of different soft classifiers. It has been demonstrated that this proposed method is an effective way to reduce the uncertainty in outputs of different soft classification, increase the recognition accuracies of boundary regions and thus increase the accuracies of SPM simulated images. (C) 2012 Elsevier B.V. All rights reserved.