the numerical sea ice forecast in finland in the winter 1993-94 ...

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20 To make the pixelwise decision about the pixel’s elass, we use the above mentioned correlation value as well as the intensity value of the pixel and the classes of the neighboring pixels. The uncertainty due to the correlation value of the pixel block is incorporated in the decision procedure through “fictive neighbors”, which modify the energy function of the pixelwise classifier. The information value of the intensity value of the pixel is somewhat vague because of the uncalibrated nature of the used SAR images. The intensity values of the SAR image gradually decrease with the increasing incidente ange1 of the radar beam. As a procedure to merge the different information sources, intensity value, spatial continuity, and the autocorrelation Iterated Conditional Mode (ICM) algorithm by Besag is used. The whole classilication scheme is presented in Figure 7. The classification procedure could identify large open water areas or ice fields with great accuracy. The classification algorithm also nicely detected ice floes, although it usually overestimated their area. On the other hand, leads or relatively narrow open water areas, were underestimated or they were, as a whole, interpreted to represent ice. Hence, the classification error was mostly one-sided, i.e. open water was interpreted as ice. However, this kind of error is more acceptable than the error in the other direction. Two ERS-1 SAR images and their classilication results are presented in Figures 8 and 9. ERS-1 SAR IMAGE I ICM I CIASSIFICATION Fig. 7. The schema of the open water / sea ice classification algorithm.
21 Fig. 8. a) ERS-1 SAR image 14.03. 1994 off Kaskinen. Original data 0 ESA 1994, dis;tributed by TronIls0 Satellite Station. Fig. 8. b) The classification res&. The light colour indicates open water.
Page 1 and 2: MER1 - Report Series of the Finnish
Page 3 and 4: THE NUMERICAL SEA ICE FORECAST IN F
Page 5 and 6: 5 (5) ah,- --$Vh,+yr,+@, at , where
Page 7 and 8: 8 v”biiRICAL SEA ICE REDICTION :Q
Page 9 and 10: 9 vu~i.,C;L SEA :CE QEDICflOX ,994
Page 11: 11 16.00 v, i 30.00 // [ j 25.00 il
Page 14 and 15: 14 considerably affect navigation -
Page 16 and 17: 16 The maximum fast ice thicknesses
Page 18 and 19: 18 finally undelined areas are area
Page 22 and 23: 22 Fig. 9..a)EEi:S-1 SAR image 05.0
Page 24 and 25: 24 Table 1. ERS-1 images and weathe
Page 26 and 27: 26 VO Date Image Area Station 6 3 O
Page 28 and 29: l0 Date Image Area 136 26,Mar ers-9
Page 30 and 31: 30 HJLU, VAASA, RAUMA, MAARIANHAMIN
Page 32 and 33: 32 Table 3. Ice and snow thickness
Page 34 and 35: 34 Date: 10.0 1.1994 09:48 UTC Imag
Page 36 and 37: 36 Date: 09.02.1994 09:48 UTC Image
Page 38 and 39: 38 Obscurity: No. Date: 12.02.1994
Page 40 and 41: 40 Ice types in the image: Leve1 ic
Page 42 and 43: 42 Area: Off Kaskinen Ice thickness
Page 46 and 47: 46 Image: ers940304-a7 Area: Off Pi
Page 48 and 49: 48 Ice types in the image: Fast ice
Page 52 and 53: 52 Verification: Fast ice is visibl
Page 54 and 55: 54 Ice types in the image: Fast ice
Page 56 and 57: 56 Verification: Very good even in
Page 60 and 61: 60 Area: Off Kaskinen Ice thickness
Page 63 and 64: SS= ‘OFMA [NE RE! drift , ice 1 9
Page 65 and 66: - n x 65
Page 67 and 68: OSlttiSkOIlSCIltr&%tiO cacbcc = Par
Page 70 and 71:
Hyvin harm ajoju Very open ice (l-3
Page 72 and 73:
Osittai.skonscnuaatio cachcc = Part
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- HAVSFORSKNINGSINSTITUTET FINNISHI
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HAVSFORSKNINGSINSTITUTET FlNNlSHlNS
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