Pixel- and Object-based High-Resolution Image Processing

Published 2/2023
Created by Ali Jamali
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 12 Lectures ( 2h 5m ) | Size: 1.7 GB

Pixel- and Object-based High-Resolution satellite Image classification in ENVI software (FAST TRACK)
What you’ll learn
Unsupervised High-resolution satellite image classification
Supervised High-resolution satellite image classification
Pixel-based High-resolution satellite image classification
Object-based High-resolution satellite image classification
K-means and Isodata unsupervised algorithms
Maximum Likelihood, Minimum Distance, and Spectral Angle Mapper algorithms
Image classification using Artificial Neural Networks
Accuracy assessment in ENVI
Object-based feature extraction from high-resolution satellite imagery
Reference data generation in ENVI
Requirements
Basics of Remote Sensing
Description
High-resolution satellite imagery classification for land cover land use mapping is a critical aspect of Earth’s surface monitoring and mapping. In this course, land cover land use mapping using the well-known ENVI software is covered. You will learn how to use supervised algorithms, such as Artificial Neural Networks (ANN) and Maximum Likelihood classifier (MLC)  to classify high-resolution satellite imagery. Pixel-based and object-based image classification is also discussed. Object-based feature extraction using high-resolution imagery is presented. You will learn how to use unsupervised algorithms, such as the k-means algorithm for satellite image clustering. The discussed 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 validation of the 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:Learn how to use unsupervised algorithms in ENVI softwareLearn how to use supervised algorithms in ENVI softwareLearn pixel-based high-resolution satellite image classificationLearn object-based high-resolution satellite image classificationLearn accuracy assessment in ENVI software
Who this course is for
Remote sensing engineers
GIS engineers
Govt sector agriculture scientists
Master students of GIS and Remote Sensing
Ph.D. students of Data science, GIS, and Remote Sensing
Homepage

www.udemy.com/course/pixel-and-object-based-high-resolution-image-processing-f/