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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Incorporating Spectral Similarity Into Markov Chain Geostatistical Cosimulation for Reducing Smoothing Effect in Land Cover Postclassification
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Incorporating Spectral Similarity Into Markov Chain Geostatistical Cosimulation for Reducing Smoothing Effect in Land Cover Postclassification

机译：将光谱相似度纳入Markov链地统计协同模拟中以减少土地覆盖后分类中的平滑效果

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摘要

Spatial statistics provides useful methods for incorporating spatial dependence into land cover classification. However, the geometric features of land cover classes are difficult to be captured by geostatistical models due to smoothing effect. The objective of this study is to incorporate spectral similarity into the Markov chain random field (MCRF) cosimulation (coMCRF) model, that is, to propose a spectral similarity-enhanced MCRF cosimulation (SS-coMCRF) model, for land cover postclassification so that postclassification will cause less geometric loss. Two mutually complementary spectral similarity measures, Jaccard index and the spectral correlation measure, were employed as a constraining factor in SS-coMCRF. One medium spatial resolution scene with a complex landscape and one very high spatial resolution scene with an urban landscape were selected for case studies. Neural network classifier and support vector machine classifier were used to conduct land cover preclassifications. Both coMCRF and SS-coMCRF were used to postprocess preclassified images based on expert-interpreted sample datasets from multiple data sources. Compared with preclassified results that depend on only spectral information of pixels, postclassifications by both models achieved similar significant improvements in overall accuracy. However, compared with coMCRF, the SS-coMCRF model apparently improved postclassified land cover patterns by effectively capturing some geometric features (e.g., boundaries and linear stripes) and more details of land cover classes. In general, incorporating spectral similarity into land cover postclassification through SS-coMCRF may contribute significantly to the “shape” or geometric accuracy of classified land cover classes.
机译：空间统计提供了将空间依赖性纳入土地覆盖分类的有用方法。然而，由于平滑作用，地统计模型很难捕获土地覆盖类别的几何特征。这项研究的目的是将光谱相似度纳入马尔可夫链随机场（MCRF）协同模拟（coMCRF）模型，即提出一种光谱相似度增强的MCRF协同模拟（SS-coMCRF）模型，用于土地覆盖物后分类，以便后分类将减少几何损失。两种相互补充的光谱相似性度量，Jaccard指数和光谱相关性度量，被用作SS-coMCRF中的约束因子。案例研究选择了一个具有复杂景观的中等空间分辨率场景和一个具有城市景观的非常高空间分辨率场景。使用神经网络分类器和支持向量机分类器进行土地覆盖物的预分类。 coMCRF和SS-coMCRF均用于根据来自多个数据源的专家解释的样本数据集对预分类的图像进行后处理。与仅依赖像素光谱信息的预分类结果相比，两个模型的后分类均在总体准确性上实现了类似的显着提高。但是，与coMCRF相比，SS-coMCRF模型通过有效地捕获某些几何特征（例如边界和线性条纹）以及更多的土地覆盖类别细节，明显改善了后分类的土地覆盖格局。一般而言，通过SS-coMCRF将光谱相似度纳入土地覆盖物的后分类可能会极大地有助于分类土地覆盖物类别的“形状”或几何精度。

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