3.1 Oyster Bed Detection by RADARSAT Data
The oyster bed detection by RADARSAT data is performed by the following 10 steps:
Step 1: Making the speckle noise reduced image with 7 by 7 median filter.
Step 2: Making the amplitude-enhanced image with 7 by 7 maximizing filter.
Step 3: Making the amplitude-unenhanced image with 7 by 7 minimizing filter.
Step 4: Subtracting the amplitude-unenhanced image from the amplitude-enhanced image and
adding an offset value with 100.
Step 5: Making the smoothing image with 7 by 7 average filtering.
Step 6: Making the binalized image with the threshold value T
R.
T
R is defined to 125 because it almost corresponds to the probability in which the half
of the pixels in the window have the positive values (at least one) after subtraction
(100+7*7/2=124.5).
Step 7: Filling the binalized image with 7 by 7 maximizing filter.
Step 8: Shrinking the binalized image with 7 by 7 median filter.
Step 9: Masking the resultant image by the land areas extracted from a map.
Step 10: Counting oyster beds.
In Step 10, the number of oyster beds is obtained by dividing the pixel count in the image of
Step 9 by the number of pixels within the window on the assumption that the window size
corresponds to the actual size of one oyster bed. However, in SAR images, the result was still
affected by a speckle noise for smaller window size (3 by 3 or 5 by 5) and finally the window
size with 7 by 7 was the best for RADARSAT-FINE images.
3.2 Oyster Bed Detection by SPOT HRV Panchromatic Data
The oyster bed extraction of SPOT HRV panchromatic data is performed by almost same
procedure as RADARSAT, but with 3 by 3 window size, as follows:
Step 1: Making the amplitude-enhanced image with 3 by 3 maximizing filter.
Step 2: Making the amplitude-unenhanced image with 3 by 3 minimizing filter.
Step 3: Subtracting the ampritude-unenhanced image from the amplitude-enhanced image and
adding an offset value with 100.
Step 4: Making the binalized image with the threshold value TS.
TS is defined to 105 by the same reason of RADARSAT (100+3*3/2=104.5).
Step 5: Filling the binalized image with 3 by 3 maximizing filter.
Step 6: Shrinking the binalized image with 3 by 3 minimizing filter.
Step 7: Masking the resultant image by the land areas extracted from a map.
Step 8: Counting oyster beds by the same method as RADARSAT.
4. Experimantal Result
The result of oyster bed detection by RADATSAT-FINE SAR (Feb. 1st, 1999) and that by SPOT
HRV panchromatic (Jan. 31, 1999) around Hiroshima Bay are shown in Figure 3. And the
results for the test site in Figure 2 are shown in Figure 4. The results for counting the number of
oyster beds using RADARSAT and SPOT are shown in Table 1 together with the official
announcement of the number of oyster beds on May 1997 .
Fig.3 Results of oyster bed detection by RADARSAT(left) and SPOT(right) for the same areas
as Figure 1.
Fig.4. Results of oyster bed detection by RADARSAT (left) and SPOT (right) in the test site
shown in Figure 2.
Table 1. Results for counting the number of oyster beds.
| Area |
Official Announcement (on May 1997) |
RADARSAT-FINE SAR (Feb. 1st, 1999) |
SPOT HRV Panchromatic (Jan. 31, 1999) |
| Test site |
333 |
322 |
329 |
| Whole Hiroshima Bay |
12972 |
12400 |
12911 |
In Figure 3, it is found that the oyster beds are located near to the mouth of rivers and around
small islands. Through the comparison of Figure 4 and Table 1, it can be seen that the result by
SPOT is slightly better than that by RADARSAT. One reason is due to the smaller window size
for SPOT than for RADARSAT. For RADARSAT, speckle noise problem in SAR image still
can not be solved completely. However, the result by RADARSAT is quite similar to that by
SPOT and the utilization of RADARSAT data for oyster beds monitoring is expected to be one
of the practical application of RADARSAT data due to a merit of all weather characteristics of
SAR data.
5. Concluding Remarks
Foregoing analyses lead to the promising application of both of RADARSAT-FINE SAR images
and SPOT HRV panchromatic images for monitoring oyster beds in wide inner bay areas. The
actual result for oyster bed detection and for counting the number of the beds was slightly better
for SPOT than for RADARSAT. However, due to the all weather characteristics of RADARSAT
SAR, RADARSAT-FINE mode data is much promising to be used for monitoring of oyster beds
in wide inner bay areas. Further case studies in different seasons with the combination of other
environmental information like water surface temperature and water quality should be continued
to proceed oyster bed monitoring by remote sensing.
Acknowledgements
We thank to National Space Development Agency of Japan (NASDA) for the corporation in
RADARSAT and SPOT data acquisition.
References
- Suga Y. and S.Tanaka, 1985. An Oyster Cultivation in Hiroshima Bay Surveyed from
LANDSAT TM Data (In Japanese), Proceedings of the 5th Japanese Conference on
Remote Sensing, RSSJ(The Remote Sensing Society of Japan), 31, pp.119-120.
- Suga Y., K.Aoki, Y.Oguro, Y.Aratani, K.Takasaki and S.Tanaka, 1997. Detection of oyster farm
beds in Hiroshima Bay using satellite data (In Japanese), Proceedings of the 23rd
Japanese Conference on Remote Sensing, RSSJ(The Remote Sensing Society of Japan)
A22, pp.59-60.
- Suga Y., Y.Oguro, S.Takeuchi and H.Ogawa, 1999. A trial for automatic detection of oyster beds
in Hiroshima Bay with satellite data (In Japanese), Proceedings of the Joint Conference
of JSPRS(Japan Society of Photogrammetry and Remote Sensing) and RSSJ(The
Remote Sensing Society of Japan) B1-4, pp.215-216.
