The document discusses the classification and analysis process for satellite imagery data. It involves classifying land covers by identifying reflectance values in the red band and near infrared band and using those values in a machine learning process to test and train a classification model and produce classification results. It also mentions resampling the original data and performing atmospheric corrections before extracting training regions and using machine learning.

1. Classification & Indices
Thursday, 16 November 2017
J.Mwaura
openmaps14@gmail.com
HANDS-ON TRAINING

2. S-2 Products
Level-0
Level-0
consolidated
Level-1a Level-1b Level-1c
Telemetry
Analysis
Decompression
Pixels re-
arrangement
Preliminary
quicklook &
cloud mask
generation
Geometric
Viewing
Model
Refinement
Radiometric
Corrections
Preview Image
& Masks
Generation
Resampling
Conversion to
Reflectance

3. Classification
• Classify land covers
– Identify Reflectance of Red (RED) band,
• Strong chlorophyll absorption region
–Band 4
– near infrared (NIR) band,
• High vegetation canopy reflectance
–Band 8a
–SWIR band – band 11

4. Analysis
Original
Data
Resampling
Atmospheric
Correction
Extract
Training
Region
Machine
Learning
Testing Training
Classification
Results

5. Thanks….