外文翻译基于穗帽变换的XX

Abstract—Landsat acquires the longest space-based moderatere-solution land remote sensing images continuously. Compared with the other earlier Landsat satellites, Landsat 8 has several new characteristics in spectral bands, spectral range and radiometric resolution. Therefore, there is a strong requirement to analyze the characteristics of the Landsat 8 for land cover classification, global change research. In this paper, Landsat 8 OLI image was used with Support Vector Machine (SVM) and Tasseled Cap Transformation (TCT) for land cover classification. Firstly, the Top of Atmospheric (TOA) reflectance based TCT was developed based on Landsat 8 OLI images. Then comparison of ISODATA, K-Means and SVM of all original eight Landsat 8 OLI bands and both of TCT Greenness and Wetness in land use and land cover classification was done. The present results indicated that compared with using the original 8 Landsat 8 OLI bands, the classification results from ISODATA and K-Means based on both of TCT Greenness and Wetness had better robustness and accuracy, and the classification using SVM with TCT had better efficiency and accuracy.摘要Landsat连续获取了最长的太空中分辨率的陆地遥感图像。

与其他早期的陆地卫星相比，Landsat 8有几个新的特征光谱，光谱范围和辐射分辨率。

因此，有必要去分析Landsat 8对于土地覆盖进行分类的特点，从而进行全球变化研究。

在本文中，Landsat 8 OLI图像采用支持向量机（SVM）和缨帽变换（TCT）来进行土地覆盖分类。

首先，由Landsat 8 OLI图像得出基于TCT的大气层顶部（TOA）的反射率。

然后将所有原八Landsat 8波段的ISODATA，k-均值和SVM与土地利用和土地覆盖分类中基于TCT的绿度和湿度进行了比较。

研究结果表明，与利用原八Landsat 8 OLI波段相比，基于TCT的绿度和湿度的ISODATA和k-均值的分类结果具有较好的鲁棒性和准确性，而且利用SVM和TCT进行的分类具有较好的效率和精度。

Keywords-classification; Landsat 8 OLI; support vector machine; tasseled cap transformation关键词-分类；Landsat 8 OLI；支持向量机；缨帽变换Land use and land cover information has been recognized as one of the key data information for many subjects of regional planning, global change research and environmental monitoring applications. Over the last four decades, remote sensing data has more and more become a principal source of land use and land cover information collection. The derivation of land use and land cover information from satellite remote sensing data has been the theme of intense interesting research in the remote sensing displine. One of the main methods to derive land use and land cover information from remote sensing data is classification. Lots of classifiers have been in use in remote sensing researches including the ISODATA, K-Means unsupervised classifiers, the Maximum Likelihood, Minimum Distance, Mahalanobis Distance, Parallelepiped, Spectral Angle Mapper, Neural Network and Decision Treeclassifiers, and most of them have been integrated in the remotely sensed image processing software such as PCI, ERDAS and ENVI. Despite the struggle, there is still considerable space for study for further increases in classification accuracy to be obtained and maximizing thedegree of land use and land cover information derivation from remote sensing images, and a strong desire to simplify the classification procedure and improve the classificationefficiency. Thus, new sensors and research into new classification methods has continued. Landsat acquires the longest space-based moderate-resolution land remotely sensed datacontinuously. Four decades of image data becomes a unique resource for those who work in geology, forestry, agriculture, regional planning, education, mapping, and global change research. On May 30, 2013, images from the Landsat 8 satellite (launched on February 11, 2013) became available. Like before, this mission sustains the acquisition of highquality image that fulfill both NASA and USGS scientific and operational demands for monitoring land use and land change. Compared with the other earlier Landsat satellites, Landsat 8 has several new characteristics in spectral bands, spectral range and radiometric resolution. Therefore, there is a strong requirement to analyze the characteristics of the Landsat 8 forland cover classification, global change research. One of new classification methods, support vector machine (SVM) have recently attracted the attention of the remote sensing researchers.土地利用与土地覆盖信息已被确认为多个区域规划学科，全球变化研究和环境监测的应用中的关键数据信息。

在过去的四年中，遥感数据越来越成为土地利用和土地覆盖信息采集的主要来源。

从卫星遥感数据推导土地利用和土地覆盖的信息已经成为遥感学科内紧张而有趣的研究主题。

从遥感数据中提取土地利用信息的主要方法之一是分类。

已在遥感研究中使用的分类包括ISODATA，k-均值非监督分类，最大似然法，最小距离，马氏距离，平行六面体，光谱角制图，神经网络和决策树分类器，其中大部分已经集成在了遥感图像处理软件如PCI，ERDAS和ENVI中。

尽管斗争，最大限度地从遥感影像获取土地利用和土地覆盖信息并进一步提高分类精度具有相当大的研究空间，并且简化分类程序和提高分类效率是非常有必要的。

因此，新的传感器和新的分类方法被持续地进行研究。