Image Analysis with ArcGIS
Strictly by pre-registration only
What is this course about?
This course covers dynamic raster processing options available in ArcGIS and takes you on an in-depth exploration of image classification. Learn best practices and workflows to enhance visualisation and extract meaningful information from satellite imagery, LiDAR, and other remotely sensed data. This course covers dynamic raster processing options available in ArcGIS and takes you on an in-depth exploration of image classification. You will use three classification methods to categorise land cover features and learn how to determine which method is appropriate for a given project and dataset.
This course is designed for GIS professionals, image analysts, and others who work with imagery for mapping and analysis. Those working in the forestry, hydrology, environmental management, urban planning, defence, intelligence, and mining industries may find the course of particular benefit.
Visualisation, Editing and Analysis Course
Safety measures for training courses
While courses in our training facility have resumed, we want to assure you that the wellbeing of our trainees and staff remains Esri Indonesia’s highest priority.
As the COVID-19 situation continues to develop, strict safety measures and guidelines as recommended by the Ministry of Health are being adhered to for all training conducted.
All computer equipment and desks are disinfected before class commences and during the lunch break, while seats are placed and marked at least 1-metre apart, in accordance with official Safe Distance Measures. Acrylic partitions have also been installed between each desk.
Masks are mandatory throughout training, and alcohol hand sanitisers and anti-bacterial wet wipes will be available.
Your temperature will be taken before commencement. For your safety, you will be asked to return home and see a doc if your temperature exceeds 37.5deg.
Are there any prerequisites?
What skills will I learn?
- Apply dynamic raster processing functions to enhance raster display, prepare data for analysis, and quickly create multiple products from a single data source
- Create a time-series mosaic dataset to visually identify and document areas of change
- Support change detection, risk assessment, and other types of analysis by performing unsupervised, supervised, and object-oriented classification
- Assess the accuracy of classification results
What can I expect?
- Course topics
Raster function chains and templates
- Applying raster functions
- Image Analysis window basics
- Using a LAS database in a mosaic dataset
Visually analysing change over time
- Determining areas of change
- Sources of raster data
- Improving the display of raster data
Introduction to image classification
- Exploring remotely sensed change
- Image classification history
- Types of image classification
- Classification outputs
Change detection through unsupervised classification
- Unsupervised classification review
- Characteristics of coarse-resolution data
- Landsat bands exploring
- Landsat data
Supervised classification of developed areas
- Supervised classification review
- Creating a spectrally pure training sample
Accuracy assessment of classified results
- Using accuracy assessments
- Components of an accuracy assessment
Impervious surface analysis with object-oriented classification
- Image segmentation
- Segmentation configuration
- Object-oriented training samples