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所在平台: Udemy |
课程主页: https://www.udemy.com/course/processing-copernicus-sentinel-2-data-using-python/
课程评论：没有评论
**课程总结：使用Python处理欧空局哨兵-2号数据** 本课程面向首次接触遥感数据的初学者，旨在教授如何利用免费的Python工具分析和处理来自欧洲哥白尼哨兵-2号（Sentinel-2）任务的免费影像。无需任何先验知识。 **课程内容概述：** * **环境搭建：** 课程将引导学员设置哥白尼数据空间生态系统（Copernicus Dataspace Ecosystem）账户，并安装必要的Python环境。 * **数据获取：** 利用Python和哥白尼数据空间生态系统API，学习如何搜索、筛选和下载Sentinel-2数据产品。 * **数据处理与分析：** 使用Python打开Sentinel-2数据产品，提取并处理可见光和近红外波段，创建RGB合成影像，并计算常用的植被指数（NDVI）和水体指数（NDWI）。 * **基础校正：** 介绍并实践基本的校正方法，如归一化和亮度校正。 * **附加应用：** （彩蛋环节）展示如何运用机器学习技术（聚类）对Sentinel-2数据进行分类，从而估算土地覆盖图。 本课程将通过循序渐进的学习过程，使学员掌握使用Python处理和分析遥感数据的基本技能。
The use of remote sensing data is growing, with the need to use such data for many applications ranging from the environment to agriculture, urban development, security and disaster management. This course is intended for beginners who would like to make their first acquaintance with remote sensing data, and learn how to use freely available tools such as Python to analyze and process freely available imagery from the Copernicus Sentinel-2 mission. No prerequisite knowledge is required.Through a step-by-step learning process, this course starts off with setting up a Copernicus Dataspace Ecosystem account, and installing a Python environment. Python is then used to make use of the Copernicus Dataspace Ecosystem API to search for, filter and download Sentinel-2 products. Also using Python, these products are then opened and the corresponding optical and near-infrared bands are analyzed and processed to create and RGB composite image, as well as calculate commonly used indices such as NDVI and NDWI. Basic correction methods such as normalization and brightness correction are also introduced.At the end of the course, a bonus application is presented, where a machine learning technique (clustering) is used to partition the content of the Sentinel-2 product into various categories to obtain an estimate for a land cover map.
