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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Classification of VHR Multispectral Images Using ExtraTrees and Maximally Stable Extremal Region-Guided Morphological Profile
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Classification of VHR Multispectral Images Using ExtraTrees and Maximally Stable Extremal Region-Guided Morphological Profile

机译：使用ExtraTrees和最大稳定的末梢区域引导形态特征对VHR多光谱图像进行分类

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摘要

Pixel-based contextual classification methods, including morphological profiles (MPs), extended MPs, attribute profiles (APs), and MPs with partial reconstruction (MPPR), have shown the benefits of using geometrical features extracted from very-high resolution (VHR) images. However, the structural element sequence or the attribute filters that are necessarily adopted in the above solutions always result in computationally inefficient and redundant high-dimensional features. To solve the second problem, we introduce maximally stable extremal regions (MSER) guided MPs (MSER_MPs) and MSER_MPs(M), which contains mean pixel values within regions, to foster effective and efficient spatial feature extraction. In addition, the extremely randomized decision tree (ERDT) and its ensemble version, ExtraTrees, are introduced and investigated. An extremely randomized rotation forest (ERRF) is proposed by simply replacing the conventional C4.5 decision tree in a rotation forest (RoF) with an ERDT. Finally, the proposed spatial feature extractors, ERDT, ExtraTrees, and ERRF are evaluated for their ability to classify three VHR multispectral images acquired over urban areas, and compared against C4.5, Bagging(C4.5), random forest, support vector machine, and RoF in terms of classification accuracy and computational efficiency. The experimental results confirm the superior performance of MSER_MPs(M) and MSER_MPs compared to MPPR and MPs, respectively, and ExtraTrees is better for spectral-spatial classification of VHR multispectral images using the original spectra stacked with MSER_MPs(M) features.
机译：基于像素的上下文分类方法，包括形态轮廓（MP），扩展MP，属性轮廓（AP）和具有部分重构的MP（MPPR），已显示出使用从超高分辨率（VHR）图像中提取的几何特征的好处。然而，以上解决方案中必须采用的结构元素序列或属性过滤器总是导致计算效率低下和冗余的高维特征。为了解决第二个问题，我们引入了最大稳定的末梢区域（MSER）引导的MP（MSER_MPs）和MSER_MPs（M），其中包含区域内的平均像素值，以促进有效而高效的空间特征提取。此外，还介绍并研究了极随机决策树（ERDT）及其集成版本ExtraTree。通过简单地用ERDT替换旋转林（RoF）中的常规C4.5决策树，提出了一种非常随机的旋转林（ERRF）。最后，对提议的空间特征提取器ERDT，ExtraTrees和ERRF进行分类，以评估其对在市区内获取的三幅VHR多光谱图像进行分类的能力，并与C4.5，Bagging（C4.5），随机森林，支持向量机进行比较，以及RoF的分类准确性和计算效率。实验结果证实了MSER_MPs（M）和MSER_MPs分别优于MPPR和MPs，并且ExtraTrees使用叠加了MSER_MPs（M）功能的原始光谱对VHR多光谱图像的光谱空间分类更好。

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