Presentation is loading. Please wait.

Presentation is loading. Please wait.

Satellite data Marco Puts

Similar presentations


Presentation on theme: "Satellite data Marco Puts"— Presentation transcript:

1 Satellite data Marco Puts
THE CONTRACTOR IS ACTING UNDER A FRAMEWORK CONTRACT CONCLUDED WITH THE COMMISSION

2 Remote Sensing Raster (Matrix) Data Format

3 What is image processing
Enhancing an image or extracting information (features) from image Routines for information extraction from remotely sensed images.

4 Image Processing Includes
Image quality and statistical evaluation Radiometric correction Geometric correction Image enhancement and sharpening Image classification Pixel based Object-oriented based Accuracy assessment of classification Post-classification and GIS Change detection

5 Image Quality Errors in remote sensor data:
the environmental errors (e.g., atmospheric scattering, cloud), random or systematic malfunction of the remote sensing system (e.g., an uncalibrated detector creates striping), or improper pre-processing of the remote sensor data prior to actual data analysis (e.g., inaccurate analog-to-digital conversion).

6 Examples

7 Noise reduction Combined Principle Component Analysis Xie et al. 2004

8 Noise reduction Gamma Maximum A Posteriori Filter Nezry et al, 1995

9 Image Statistics Univariate image statistics
average, variance, min, max, etc. Multivariate image statistics How much emmitance from more than one band PCA, feature extraction, etc.

10 Types of radiometric correction
Detector error or sensor error (internal error) Atmospheric error (external error) Topographic error (external error)

11 Atmospheric error

12 Atmospheric error absolute

13 Atmospheric error dark frame subtraction

14 Cloud removal Mean based cloud removal Second highest value algorithm
Use mean value of cloudy regions. Second highest value algorithm Detect clouds based on second highest values per row Modified Maximum Averaging Iteratively remove pixels brighter than the average Hybrid

15 Topographic correction
Sun Topographic correction Earth Sat

16 Geometric correction Geocoding: geographical referencing Registration
Image to Map (or Ground Geocorrection) Image to Image Geocorrection Spatial interpolation Intensity interpolation (Resampling)

17 Image enhancement Resolution adjustment,
contrast adjustments (linear and non-linear), band rationing, spatial filtering, fourier transformations, principle component analysis, texture transformations, and image sharpening

18 Classification Unsupervised Supervised Classes can be hard to identify
Information about a class may not be in image

19 Example ABS: Estimating Crop Area Stats

20 Example ABS: Estimating Crop Area Stats
Classification: Support Vector Machines Gaussian ML classification Multinomial Logistic Regression

21 Example: Statcan: Crop condition assessment
Processing steps: Data Import Reprojection and Clipping Cloud Detection and Removal Computing statistics by region

22 Reprojection and Clipping

23 Cloud Detection and Removal

24 Cloud Detection and Removal

25 Computing Statistics by Region

26 Sentinel Toolbox Rich sets of tools for analyzing satellite data


Download ppt "Satellite data Marco Puts"

Similar presentations


Ads by Google