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.
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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)
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The SPOT-XS image has to be resized by a factor of 2.0
 Basic Tools → Resize Data (Spatial/Spectral)
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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
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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
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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
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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
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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)