1. Data Merging and GIS Integration
Chapter
Data Merging and GIS Integration
Analysis and applications of remote sensing imagery
Instructor: Dr. Cheng-Chien Liu
Department of Earth Sciences
National Cheng Kung University
Last updated: 17 April 2017

2. 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

3. 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)

4. Change detection procedures
Types of interest
Short term phenomena: e.g. snow cover, floodwater
Long tern phenomena: e.g. urban fringe development, desertification
Ideal conditions
Same sensor, spatial resolution, viewing geometry, time of day
An ideal orbit: ROCSAT-2
Anniversary dates
Accurate spatial registration
Environmental factors
Lake level, tidal stage, wind, soil moisture condition, …

5. Change detection procedures (cont.)
Approach
Post-classification comparison
Independent classification and registration
Change with dates
Classification of multi-temporal data sets
A single classification is performed on a combined data sets
Great dimensionality and complexity  redundancy
Principal components analysis
Two or more images  one multiband image
Uncorrelated principal components  areas of change
Difficult to interpret or identify the change
Plate 32: (a) before (b) after (c) principal component image

6. Change detection procedures (cont.)
Approach (cont.)
Temporal image differencing
One image is subtracted from the other
Change-no change threshold
Add a constant to each difference image for display purpose
Temporal image ratioing
One image is divided by the other
No change area  ratio  1
Change vector analysis
Fig 7.55
Change-versus-no-change binary mask
Traditional classification of time 1 image
Two-band (one from time 1 and the other from time 2)  algebraic operation  threshold  binary mask  apply to time 2 image

7. Change detection procedures (cont.)
Approach (cont.)
Delta transformation
Fig 7.56
(a): no spectral change between two dates
(b): natural variability in the landscape between dates
(c): effect of uniform atmospheric haze differences between dates
(d): effect of sensor drift between dates
(e): brighter or darker pixels indicate land cover change
(f): delta transformation
Fig 7.57: application of delta transformation to Landsat TM images of forest

8. Multisensor image merging
Plate 33: IHS multisensor image merger of SPOT HRV, landsat TM and digital orthophoto data
Multi-spectral scanner + radar image data

9. Merging of image data with ancillary data
Image + DEM
 synthetic stereoscopic images
Fig 7.58: synthetic stereopari 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

10. 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)

11. Incorporating GIS data in automated land cover classification (cont.)
Multi-source image classification decision rules (user-defined)
Plate 34: a composite land cover classification
A supervised classification of TM image in early May
A supervised classification of TM image in late June
A supervised classification of both dates combined using a PCA
A wetlands GIS layer
A road DLG (digital line graph)
Table 7.5: basis for sample decision rules

12. Incorporating GIS data in automated land cover classification (cont.)
Artificial neural networks