Transcript Chapter 8 Remote Sensing & GIS Integration Basics EM spectrum: fig p.
Chapter 8 Remote Sensing & GIS Integration
Basics
EM spectrum: fig p. 268
reflected emitted detection
film sensor atmospheric attenuation
Recording type
analog (film)
must retrieve film resolution based on film type digital
easier to retrieve data resolution based on sensors/unit area RASTER DATA
System classifications
Passive systems
use existing source of EM illumination Active systems
provide source of EM illumination
Platforms
airplane
low & high altitude
high resolution large scale satellite
various altitudes low to high resolution small to large scale
Imaging characteristics
spatial resolution
most important characteristic basis
lens
film or sensor ground resolution
spatial resolution scale
Imaging characteristics
spectral resolution
EM wavelengths to which a system is sensitive
components
number of bands (more is better)
width of bands (narrow is better) radiometric
differences between “steps” in exposure
contrast temporal (daily, monthly, yearly, etc.)
Selecting image characteristics
“appropriate” specifications
ground resolution
bands & widths spectral resolution determine
what you need to observe what you might want in the future what you can afford
Photogrammetry
obtaining reliable measurements from images
science art scale - based on:
focal length height of plane average terrain elevation
Photogrammetry
sources of error
relief displacement (due to central perspective) aircraft tilt orthophotographs/orthoimages
correct for above errors use digital elevation model (DEM)
Photogrammetry
thermal infrared (TIR)
sense heat systems: TIMS & ATLAS panoramic distortion (fig p. 280)
Photogrammetry
side-looking airborne radar (SLAR)
oblique view (side view)
feature foreshortening (compression of features tilted toward radar) incidence angle varies with distance from radar resolution varies with
pulse length
antenna size
Photogrammetry
satellite
all types of images
advantages
wider coverage
tilt-free
little relief displacement disadvantages
low spatial resolution (Landsat TM is 30m, SPOT is 20m)
Extraction of Data
steps (fig p. 290)
detection identification analysis and deduction classification theorization (verify/nullify hypotheses)
Image elements
tone/color – least complex size shape texture pattern height shadow association pattern – most complex
Computer-assisted classification
classifying raster data automate of low complexity functions preprocessing: radiometric & geometric correction classification approaches
supervised – classes assigned - fig p. 293 unsupervised - cluster analysis hybrid – unsupervised followed by supervised
Computer-assisted classification
types of classifiers
hard vs soft – fig p. 295 contextual – looks at neighboring pixels artificial neural networks
complex determinations based on multiple inputs - fig p. 296 field checking
Change detection
overlay
map-to-map image-to-image output
matrix map fig p. 297
Integration of GIS & Remote Sensing
requirements
same georeferencing system
rectify or register
resampling problem: raster-vector data styles three stages – fig p 299
separate but equal
seamless integration (ArcView) total integration