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A model-based performance test for forest classifiers on remote-sensing imagery

机译:基于模型的森林分类器遥感影像性能测试

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摘要

Ambiguity between forest types on remote-sensing imagery is a major cause of errors found in accuracy assessments of forest inventorymaps. This paper presents a methodology, based on forest plot inventory, ground measurements and simulated imagery, for systematically quantifying these ambiguities in the sense of the minimum distance (MD), maximum likelihood (ML), and frequency-based (FB) classifiers. The method is tested with multi-spectral IKONOS images acquired on areas containing six major communities (oak, pine, fir, primary and secondary high tropical forests, and avocado plantation) of the National Forest Inventory (NFI) map in Mexico. A structural record of the canopy and optical measurements (leaf area index and soil reflectance) were performed on one plot of each class. Intra-class signal variation was modelled using the Discrete Anisotropic Radiative Transfer (DART) simulator of remote-sensing images. Atmospheric conditions were inferred from ground measurements on reference surfaces and leaf optical properties of each forest type were derived from the IKONOS forest signal. Next, all forest types were simulated, using a common environmental configuration, in order to quantify similarity among all forest types, according to MD, ML and FB classifiers. Classes were considered ambiguous when their dissimilarity was smaller than intra-class signal variation. DART proved useful in approximating the pixel value distribution and the ambiguity pattern measured on real forest imagery. In the case study, the oak forest and the secondary tropical forest were both distinguishable from all other classes using an MD classifier in a 25 m window size, whereas pine and primary tropical forests were ambiguous with three other classes using MD. By contrast, only two pairs of classes were found ambiguous for the ML classifier and only one for the FB classifier in that same window size. The avocado plantation was confounded with the primary tropical forest for all classifiers, presumably because the reflectance of both types of forest is governed by a deep canopy and a similar shadow area. We confronted the results of this study with the confusion matrix from the accuracy assessment of the NFI map. An asset of this model-basedmethod is its applicability to a variety of sensor types, eco-zones and class definitions.
机译:遥感影像上森林类型之间的歧义是在森林清单图准确性评估中发现错误的主要原因。本文提出了一种基于林地清单,地面测量值和模拟图像的方法,可以从最小距离(MD),最大似然(ML)和基于频率(FB)的分类器的意义上系统地量化这些模糊性。该方法用多光谱IKONOS图像进行了测试,该图像在墨西哥国家森林清单(NFI)地图上包含六个主要社区(橡树,松树,冷杉,主要和次要热带热带森林以及鳄梨人工林)的区域中采集。对每个类别的一个图进行了冠层的结构记录和光学测量(叶面积指数和土壤反射率)。使用遥感图像的离散各向异性辐射传输(DART)模拟器对类内信号变化进行建模。根据参考表面的地面测量推断大气条件,并根据IKONOS森林信号得出每种森林类型的叶片光学特性。接下来,根据MD,ML和FB分类器,使用通用的环境配置对所有森林类型进行了模拟,以量化所有森林类型之间的相似性。当类的差异小于类内信号的变化时,它们被认为是模棱两可的。 DART被证明有助于逼近真实森林图像上测得的像素值分布和歧义模式。在案例研究中,使用25 m窗口大小的MD分类器,可以将橡树林和次生热带森林与所有其他类别区分开,而使用MD将松树林和原始热带森林与其他三个森林区分开。相比之下,在相同的窗口大小中,仅ML分类器发现两对类不明确,而FB分类器发现只有两类。对于所有分类器,鳄梨种植园都与原始热带森林混淆,大概是因为两种类型的森林的反射率都由一个深层的树冠和一个相似的阴影区域控制。我们使用来自NFI地图准确性评估的混淆矩阵来面对这项研究的结果。这种基于模型的方法的优势在于其对各种传感器类型,生态区和类别定义的适用性。

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