|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/hyperspectral-satellite-image-classification-using-deep-cnns/
课程评论：没有评论
课程名称：使用深度卷积神经网络进行高光谱卫星图像分类 课程概述：土地覆盖制图是地球表面监测与制图的重要环节。在本课程中，您将学习利用高光谱卫星影像进行土地利用与覆盖制图。课程内容包括如何在Google Colab中开发一维、二维、三维和混合卷积神经网络（CNNs）。所讨论和开发的方法可用于不同对象/特征的提取与制图，比如从高分辨率卫星影像中提取城市区域。遥感技术是识别和分类不同土地类型、评估植被状况以及估计环境变化的强大工具。使用Google Colab将显著减少软件和平台（如Anaconda）所遇到的问题，降低库安装需求，从而加快和提高分类地图生成的可靠性。同时，课程还涉及开发模型的验证。总而言之，遥感和地理信息系统（GIS）技术广泛应用于土地覆盖制图，提供准确和及时的信息，对于监测和管理自然资源至关重要。 课程亮点： 1. 学习卷积神经网络（CNNs）的概念 2. 学习如何开发CNN模型 3. 学习如何使用Python编程语言对高光谱卫星影像进行分类 4. 学习如何验证CNN模型 5. 学习如何从Google Drive读取和导入数据到Google Colab 6. 利用不同变种的CNN模型进行高光谱卫星数据的土地利用和覆盖制图 7. 学习如何验证机器学习模型
Land cover mapping is a critical aspect of Earth's surface monitoring and mapping. In this course, Land Use Land Cover Mapping utilizing Hyperspectral satellite imagery is covered. You will learn how to develop 1-Dimensional, 2-Dimensional, 3-Dimensional, and Hybrid Convolutional Neural Networks (CNNs) using Google Colab. The discussed and developed methods can be utilized for different object/feature extraction and mapping (i.e., urban region extraction from high-resolution satellite imagery). Remote sensing is a powerful tool that can be used to identify and classify different land types, assess vegetation conditions, and estimate environmental changes. The use of Google Colab will significantly help you to decrease the issues encountered by software and platforms, such as Anaconda. There is a much lower need for library installation in the Google Colab, resulting in faster and more reliable classification map generation. The validation of the developed models is also covered. In summary, remote sensing and GIS technologies are widely used for land cover mapping. They provide accurate and timely information that is critical for monitoring and managing natural resources.Highlights:1. Learn the concepts of Convolutional Neural Networks (CNNs)2. Learn how to develop CNN models3. Learn how to classify Hyperspectral satellite imagery using python programming language4. Learn how to validate a CNN model5. Learn to read and import your data from your Google Drive into Google Colab6. Map Land use land covers utilizing Hyperspectral satellite data with different variations of CNN models7. Learn how to validate a machine-learning model
