Pixel- and Object-based High-Resolution Image Processing

Pixel- and Object-based High-Resolution satellite Image classification in ENVI software (FAST TRACK)

Udemy

platform

English

language

Other

What you will 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

Why take this course?

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 software

Learn how to use supervised algorithms in ENVI software

Learn pixel-based high-resolution satellite image classification

Learn object-based high-resolution satellite image classification

Learn accuracy assessment in ENVI software

Content

Introduction

ISODATA algorithm

Spectral Angle Mapper

Supervised image classification

Maximum Likelihood algorithm

Mahalanobis Distance classification algorithm

SVM kernel types

K-means algorithm

Artificial Neural Networks

ANN algorithm

ANN algorithm 2

Activation functions

Accuracy assessment

Object-based feature extraction