Hyperspectral satellite image classification Using Deep CNNs
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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
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Load Hyperspectral dataIncludes how you can load a Hyperspectral satellite imagery into Google Colab.
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1-Dimensional CNN development
Includes how you can develop a 1-D CNN model.
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1-D CNN model Training
Includes how you can Train a 1-D CNN model.
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Hyperspectral satellite imagery classification using a 1-D CNN model
Includes the Hyperspectral satellite imagery classification using a 1-D CNN model.
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Points
Number of classes in Pavia dataset.
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9
Load Hyperspectral satellite data
Includes how you can load a Hyperspectral satellite imagery.
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10
2-D CNN model development
Includes the development of a 2-D CNN model.
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2-D CNN model training
Includes 2-D CNN model training.
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Hyperspectral satellite imagery classification utilizing a 2-D CNN model
Includes Hyperspectral satellite imagery classification using a 2-D CNN model.
