Data Merging
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Transcript Data Merging
Chapter 6
Data Merging
Analysis and applications of remote
sensing imagery
Instructor: Dr. Cheng-Chien Liu
Department of Earth Sciences
National Cheng Kung University
Last updated: 14 June 2005
Introduction
RS applications data merging unlimited
variety of data
• Multi-resolution data fusion
• Plate 1: GIS (soil erodibility + slope information)
Trend
• Boundary between DIP and GIS blurred
• Fully integrated spatial analysis systems norm
Content of this chapter
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Multi-temporal data merging
Multi-sensor image merging
Multi-image merging
Merging of image data with ancillary data
Multi-temporal data merging
Same area but different dates composites
visual interpretation
• e.g. agricultural crop
• Plate 31(a): mapping invasive plant species
NDVI from Landsat-7 ETM+
March 7 blue
April 24 green
October 15 red
GIS-derived wetland boundary eliminate the interpretation of false positive
areas
• Plate 31(b): mapping of algae bloom
• Enhance the automated land cover classification
Register all spectral bands from all dates into one master data set
More data for classification
Principal components analysis reduce the dimensionality manipulate, store, classify, …
• Multi-temporal profile
Fig 7.54: greenness. (tp, s, Gm, G0)
Multi-sensor image merging
Multi-sensor image merging
• Plate 33: IHS multisensor image merger of
SPOT HRV, landsat TM and digital orthophoto
data
Multi-spectral scanner + radar image
data
Exercise 1
Data Fusion
• The process of combining multiple image layers into a single
composite image
• Enhance the spatial resolution of multispectral datasets using
higher spatial resolution panchromatic data or singleband
SAR data.
Landsat TM and SPOT data fusion
• File → Open External File → IP Software → ER Mapper
Subdirectory: lontmsp
File: lon_tm.ers
Load RGB to display a true-color Landsat TM image
• File → Open External File → IP Software → ER Mapper
Subdirectory: lontmsp
File: lon_spot.ers
Load Band to display the gray scale SPOT image
Exercise 1 (cont.)
Landsat TM and SPOT data fusion (cont.)
• Resize Images to Same Pixel Size
Check spatial dimensions (2820 x 1569) and (1007 x 560)
The Landsat data: 28 meters
The SPOT data: 10 meters
The Landsat image has to be resized by a factor of 2.8 to create 10 m data that matches the SPOT data
Basic Tools → Resize Data (Spatial/Spectral)
choose the lon_tm image
Resize Data Parameters
Enter a value of 2.8 into the xfac text box
Enter a value of 2.8009 into the yfac text box
Tools → Link → Link Displays
• Perform Manual HSI Data Fusion
• Forward HSV Transform
Transform → Color Transforms → RGB to HSV
Select the resized TM data as the RGB image from the Display
Display the Hue, Saturation, and Value images as gray scale images or an RGB.
Create a Stretched SPOT Image to Replace TM Band Value
Basic Tools → Stretch Data
File: lon_spot
Data Stretching
Output Data: 0 for the Min and 1.0 for the Max
Exercise 1 (cont.)
Landsat TM and SPOT data fusion (cont.)
• Inverse HSV Transform
Transform → Color Transforms → HSV to RGB
Select the transformed TM Hue and Saturation bands as the H and S bands
Choose the stretched SPOT data as the V band
Display Results
• ENVI Automated HSV Fusion
Transform → Image Sharpening → HSV from the ENVI main menu.
Select Input RGB Input Bands dialog
Choose the TM image RGB bands
High Resolution Input File dialog
Choose the SPOT image
HSV Sharpening Parameters dialog
File: lontmsp.img
Display Results, Link and Compare
• Color Normalized (Brovey) Transform
Try the same process using
Transform → Image Sharpening → Color Normalized (Brovey)
Exercise 2
SPOT PAN and XS fusion
• File → Open Image File
Subdirectory: brestsp
File: s_0417_2.bil
Load RGB to display a falsecolor infrared SPOT-XS image with 20 m spatial resolution
• File → Open Image File
File: s_0417_1.bil
Load Band to display the SPOT Panchromatic data.
• Resize Images to Same Pixel Size
Check spatial dimensions (2835 x 2227) and (1418 x 1114)
The SPOT-XS image has to be resized by a factor of 2.0
Basic Tools → Resize Data (Spatial/Spectral)
Choose the SPOTXS image (s_0417_2.bil)
Resize Data Parameters dialog
Enter a value of 1.999 into the xfac
Enter a value of 1.999 into the yfac
Tools → Link → Link Displays
• Fuse Using ENVI Methods
Transform → Image Sharpening → HSV
Select Input RGB Input Bands
High Resolution Input File
HSV Sharpening Parameters dialog
Display and Compare Results
Self test 1
Formorsat-2 image sharpening
• File:
Pan: RS2_103048000_02_0001_PAN.tif
MS: RS2_103049000_02_0001_MS.tif
• Subscene (at least 1000 x 1000)
• Co-registration
• Image sharpening
Exercise 3
Landsat TM and SAR Data Fusion
• Read and Display Images
File → Open Image File
Subdirectory: rometm_ers
File: rome_ers2
Load Band
File → Open Image File
File: rome_tm
Load RGB to display a false-color infrared Landsat TM image with 30m spatial resolution
• Register the TM images to the ERS image
Map → Registration → Select GCPs: Image-to-Image
Base Image: Display #1 (the ERS data)
Warp Image: Display #2 (the TM data)
File → Restore GCPs from ASCII
Ground Control Points Selection dialog
GCP file: rome_tm.pts
Options → Warp File
File: rome_tm
Registration Parameters dialog
Change Output Parameters
Enter 1 for the Upper Left Corner (XO),
Enter 1 for the Upper Left Corner (YO)
Enter 5134 for the Number of Samples
Enter 5549 for the Number of Lines
Exercise 3 (cont.)
Landsat TM and SAR Data Fusion
(cont.)
• Perform HSI Transform to Fuse Data
Transform → Image Sharpening → HSV
Select Input RGB Input Bands
High Resolution Input File dialog
Choose the ERS-2 image
• Display and Compare Results
Multi-image merging
Mosaicking (鑲嵌)
• The art of combining multiple images into a single
composite image
No-georeferenced images
Georeferenced images
• Feathering
Edge feathering
The edge is blended using a linear ramp that averages the two images across the specified
distance
Specified distance = XX pixels, top image = XX%, bottom image = XX%
Cutline feathering
The annotation file must contain a polyline defining the cutline that is drawn from edgeto-edge and a symbol placed in the region of the image that will be cut off.
Exercise 4
Pixel-Based Mosaicking
• Map → Mosaicking → Pixel Based
Pixel Based Mosaic dialog
Import → Import Files
avmosaic directory
File: dv06_2.img.
Mosaic Input Files dialog
File: dv06_3.img.
Mosaic Input Files dialog, hold down the Shift key and click on the dv06_2.img and
dv06_3.img filenames to select them.
• Select Mosaic Size dialog
X Size: 614
Y Size: 1024
Pixel Based Mosaic dialog, click on the dv06_3.img filename.
YO: 513
File → Apply
• Create a virtual mosaic
File → Save Template
Output Mosaic Template
• Display the mosaicked image
Exercise 4 (cont.)
Pixel-Based Mosaicking (cont.)
• Positioning two images into a composite mosaic image
Options→Change Mosaic Size
Select Mosaic Size dialog
X Size 768
Y Size 768
Left-click within the green graphic outline of image #2
Drag the #2 image to the lower right hand corner of the diagram.
Right-click within the red graphics outline of image #3 and select Edit Entry
Data Value to Ignore: 0
Feathering Distance: 25
Repeat the previous two steps for the other image.
File → Save Template
Load Band
No feathering is performed when using virtual mosaic.
File → Apply
Background Value of 255
Display
Compare the virtual mosaic and the feathered mosaic using image linking and dynamic overlays
Exercise 5
Map Based Mosaicking
• Map → Mosaicking → Georeferenced
File → Restore Template
File: lch_a.mos
• Optionally Input and Position Images
Images will automatically be placed in their correct geographic locations The location and
size of the georeferenced images will determine the size of the output mosaic.
• View the Top Image, Cutline and Virtual, Non-Feathered Mosaic
Load Band: lch_01w.img
Right-click to display the shortcut menu and select Toggle → Display Scroll Bars to turn
on scroll bars
Overlay → Annotation
File → Restore Annotation
File: lch_01w.ann
Load Band: lch_02w.img
• File → Open Image File
File: lch_a.mos
• Create the Output Feathered Mosaic
File → Apply
• Compare
Exercise 6
Color Balancing During Mosaicking
• Create the Mosaic Image without Color Balancing
Map → Mosaicking → Georeferenced
Import → Import Files
Open File: avmosaic directory, File: mosaic1_equal.dat
Open File: avmosaic directory, File: mosaic_2.dat
select the mosaic_2.dat file, then hold down the Shift key and select the
mosaic1_equal.dat file
Show RGB color composites of these multispectral images
Edit Entry
Mosaic Display, choose RGB.
For Red choose 1, for Green choose 2, and for Blue choose 3
Repeat
Two images are stretched independently
Exercise 6 (cont.)
Color Balancing During Mosaicking (cont.)
• Output the Mosaic Without Color Balancing
File → Apply
The seams between the two images are quite obvious
• Output the Mosaic With Color Balancing
mosaic1_equal.dat
Edit Entry.
Color Balancing: Adjust.
mosaic_2.dat
Edit Entry.
Color Balancing: Fixed
File → Apply
Color Balance using
stats from overlapping regions/
stats from complete files
Display
The seams between the two images are much less visible
Merging of image data with ancillary
data
Image + DEM
• synthetic stereoscopic images
Fig 7.58: synthetic stereopair generated from a single
Landsat MSS image and a DEM
Standard Landsat images fixed, weak stereoscopic effect in the relatively
small areas of overlap between orbit passes
• Produce perspective-view images
Fig 7.59: perspective-view image of Mount Fuji
Exercise 7
3D visualization
• Loading a 3D SurfaceView
File: c:/RSI/envidata/bh_3d/bhtmsat.img
• Open and Display the DEM as a Grayscale Image
File: c:/RSI/envidata/bh_3d/bhdemsub.img
• Start the ENVI 3D SurfaceView Function
Topographic → 3D SurfaceView
Parameters
use the lowest resolution (64) while determining the best flight path. Then a higher resolution can be
used to display final fly-through sequence
• Interactive Control of 3D Visualization
• The 3D SurfaceView Positioning Dialog
• Building and Playing a User-Defined Visualization Sequence
Options → Motion Controls
Exercise 7 (cont.)
3D visualization (cont.)
• Using ENVI Annotation to Build a Visualization
Sequence
Options → Motion:Annotation Flight Path
Input Annotation from File: bhdemsub.ann
Choosing the 1st annotation object (a green polyline)
Enter Frames of 500
Enter a Flight Smooth Factor of 1000
Enter a Flight Clearance of 1000
Set the Up/Down look angle to -60.
Leave the Right/Left look angle at 0
Try flying over the surface at a constant elevation
Clicking the arrow radio button until Flight Clearance appears
Entering the desired elevation above sea level
Saving Visualizations and Output
Incorporating GIS data in automated
land cover classification
Useful GIS data (ancillary data)
• Soil types, census statistics, ownership
boundaries, zoning districts, …
Geographic stratification
• Ancillary data geographic stratification
classification
• Basis of stratification
Single variable: upland wetland, urban rural
Factors: landscape units or ecoregions that combine several
interrelated variables (e.g. local climate, soil type,
vegetation, landform)