Hyperspectral satellite image classification Using Deep CNNs

Hyperspectral satellite imagery classification Using 1-D, 2-D, 3-D, and Hybrid Convolutional Neural Networks (CNNs)

3.62 (42 reviews)
Udemy
platform
English
language
Engineering
category
instructor
Hyperspectral satellite image classification Using Deep CNNs
188
students
2 hours
content
Feb 2023
last update
$49.99
regular price

What you will learn

Concepts/basics of Convolutional Neural Networks

Visualizing Hyperspectral data in Google Colab

1-Dimensional Convolutional Neural Network development

2-Dimensional Convolutional Neural Network development

3-Dimensional Convolutional Neural Network development

Deep machine learning models training and validation in Google Colab

Developing different advanced machine learning algorithms in Google Colab

Land Use Land Cover (LULC) mapping with Hyperspectral satellite imagery

Description

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 models

3. Learn how to classify Hyperspectral satellite imagery using python programming language

4. Learn how to validate a CNN model

5. Learn to read and import your data from your Google Drive into Google Colab

6. Map Land use land covers utilizing Hyperspectral satellite data with different variations of CNN models

7. Learn how to validate a machine-learning model

Content

Introduction

Introduction
Required Concepts
Basics of Convolutional Neural Networks (CNNs)

1-Dimensional CNNs

Load Hyperspectral data
1-Dimensional CNN development
1-D CNN model Training
Hyperspectral satellite imagery classification using a 1-D CNN model
Points

2-Dimensional CNNs

Load Hyperspectral satellite data
2-D CNN model development
2-D CNN model training
Hyperspectral satellite imagery classification utilizing a 2-D CNN model

3-Dimensional CNNs

Load Hyperspectral satellite imagery
3-D CNN development
3-D CNN model training
3-D CNN model validation
Hyperspectral satellite imagery classifiaction-1
Hyperspectral satellite imagery classifiaction-2
Hybrid CNN development

Reviews

Alexander
February 1, 2023
i am expert in remote sensing and this Course is very essential in classifying and analyzing satellite image in cloud

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5125818
udemy ID
1/31/2023
course created date
2/5/2023
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