高光谱和多光谱图像在树种检测中的应用综述

Sude Gul Yel, Esra Tunc Gormus
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引用次数: 0

摘要

树种分类为森林监测、森林可持续经营和规划提供了重要数据。近年来,多光谱(MS)和高光谱(HS)遥感成像传感器的发展使树种的检测变得更加容易和准确。通过系统的综述研究,旨在了解利用多光谱和高光谱成像数据在树种检测中的贡献,同时强调该领域的最新进展,强调重要方向以及未来研究的新可能性。本文主要介绍了多光谱和高光谱图像的处理步骤,以及多光谱和高光谱图像在树种检测应用领域的优势。这样将有助于利用卫星数据。这也将为使用商业多光谱和超光谱成像数据提供经济收益。此外,还需要注意的是,由于从每种树类型中获得的光谱标签数量不同,因此处理方法和分类方法都会发生相应的变化。本综述根据所利用的数据(仅高光谱图像、仅多光谱图像及其组合)、监测的树木类型和使用的处理方法对研究进行了分组。然后,根据分类的准确性、合适的树类型和分类方法,对研究中使用的图像数据的贡献进行评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploiting hyperspectral and multispectral images in the detection of tree species: A review
Classification of tree species provides important data in forest monitoring, sustainable forest management and planning. The recent developments in Multi Spectral (MS) and Hyper Spectral (HS) Imaging sensors in remote sensing have made the detection of tree species easier and accurate. With this systematic review study, it is aimed to understand the contribution of using the Multi Spectral and Hyper Spectral Imaging data in the detection of tree species while highlighting recent advances in the field and emphasizing important directions together with new possibilities for future inquiries. In this review, researchers and decision makers will be informed in two different subjects: First one is about the processing steps of exploiting Multi Spectral and HS images and the second one is about determining the advantages of exploiting Multi Spectral and Hyper Spectral images in the application area of detecting tree species. In this way exploiting satellite data will be facilitated. This will also provide an economical gain for using commercial Multi Spectral and Hyper Spectral Imaging data. Moreover, it should be also kept in mind that, as the number of spectral tags that will be obtained from each tree type are different, both the processing method and the classification method will change accordingly. This review, studies were grouped according to the data exploited (only Hyper Spectral images, only Multi Spectral images and their combinations), type of tree monitored and the processing method used. Then, the contribution of the image data used in the study was evaluated according to the accuracy of classification, the suitable type of tree and the classification method.
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