Class 12 Assessment of Classification Accuracy Error Matrix (confusion matrix) User’s Accuracy Producer’s Accuracy Overall Accuracy Kappa Statistics RADARSAT principles and applications
Error Matrix Ground Truth Predicted Verbyla 8.0 1 2 3 4 5 Row total 40 43 30 12 25 50 52 32 Column total 42 33 37 53 35 200 class class Predicted Verbyla 8.0
Overall Classification Accuracy It is the total number of correct class predictions (the sum of the diagonal cells) divided by the total number of cells. In this case, it is (40+30+25+50+32)/200 =88%
Producer’s and user’s accuracy by cover type class Producer’s Accuracy User’s Accuracy 1 40/42=95% 40/43=93% 2 30/33=91% 30/43=70% 3 25/37=68% 25/30=83% 4 50/53=94% 50/52=96% 5 32/35=91% 32/32=100%
Kappa Statistic KHAT= In this case, KHAT=(0.88-0.21)/(1-0.21)=0.85 Overall Classification Accuracy – Expected Classification Accuracy KHAT= 1 – Expected Classification Accuracy The expected classification accuracy is the accuracy expected based on chance, or the expected accuracy if we randomly assigned class values to each pixel. In this case (see the next slide), it is (1806+1419+1110+2756+1120)/40,000=21% In this case, KHAT=(0.88-0.21)/(1-0.21)=0.85
Products for KHAT Ground Truth Predicted 1 2 3 4 5 Row total (error matrix) 1806 1419 1591 2279 1505 43 1260 990 1110 1590 1050 30 2184 1716 1924 2756 1820 52 1344 1056 1184 1696 1120 32 Column total (error matrix) 42 33 37 53 35 200 class class Predicted
Basics of Active Microwave (Radar) Sensing Bands: Ka (0.75-1.1 cm), K (1.1-1.67 cm), Ku (1.67-2.4 cm), X (2.4-3.75 cm), C (3.75-7.5 cm), S (7,2-15 cm), L (15-30 cm), P (30-100 cm) 2. Polarization: HH, HV, VH, VV Like polarized, HH, VV Cross polarized: HV, VH Source Magnetic Component Electric Component V H
Radar Signatures AZIMUTH (FLIGHT) DIRECTION TERRAIN FEATURE DEPRESSION ANGLE TRANSMITTED PULSE AZIMUTH (FLIGHT) DIRECTION TERRAIN FEATURE RANGE (LOOK) DIRECTION DIFFUSE SURFACE SIGNATURE RADAR SPECULAR (SMOOTH) SURFACE RETURN INTENSITY REFLECTORS CORNER SHADOW HIGHLIGHT TIME (Far Range) (Near Range) IMAGE TONE
3. Surface interaction Geometric characteristics: Rayleigh criterion: Smooth surface: RMS of height < /8 Rough surface: RMS of height > /8 (ii) Electrical characteristics: Dielectric constant: low when dry, 3-8 high when wet, up to 80 Dielectric constant , RADAR backscatter
3. Surface interaction (continued) (iii) Soil: Combined effects of roughness and wetness (iv) Vegetation: Combined effects of roughness and wetness Strong volume scattering for vegetation components having sizes similar to RADAR wavelength
3. Surface interaction (continued) (v) Water and Ice: Smooth water – specular reflection X band is useful for ice type, thickness L band is useful for ice extent Ice age, surface roughness, internal geometry, temperature and snow cover have effects on radar backscatter
Red River, Manitoba, Canada. Spring 1996 Flood Monitoring Red River, Manitoba, Canada. Spring 1996 Smooth, standing water appears very dark on this radar image (A). These dark tones clearly stand out against the non-flooded areas shown in the grey tones. Where standing water is present under trees or bushes, a special condition is created, called corner reflection, and these areas appear very bright (B). The town of Morris can be identified in the centre of the image, as a light rectangle (C). The town was protected by a levee and was not flooded. http://www.ccrs.nrcan.gc.ca/ccrs/tekrd/radarsat/images/man/rman01e.html
Gulf of St Lawrence, Prince Edward Island, Canada March 6, 1996 Ice type detection Gulf of St Lawrence, Prince Edward Island, Canada March 6, 1996 A: Nilas Ice B: Pancake ice C: Tears
Glacier Flow Measurement Slessor Glacier, Antarctica September 24 & October 18, 1997 (S2)
Automated Ship & Oil Slick Detection Flemish Cap, East Coast of Canada August 2, 1996