One of the remarkable characteristics of synthetic aperture radar (SAR) is to record physical value called backscattering coefficient of the earth's surface, regardless of weather condition or sun illumination. Therefore, SAR is a powerful tool which can be utilized to develop a universal method to fully comprehend damaged areas in natural hazards such as earthquakes, forest fires and floods. We performed a feasibility study on backscattering characteristics of damaged areas in the 1995 Hyogoken-Nanbu (Kobe), Japan earthquake using the pre- and postevent ERS-1 images, revealing that the backscattering coefficient and intensity correlation between the two attained values were significantly lowered in hard-hit areas. The evaluation, however, was performed without speckle noise reduction. In this study, we investigated the effect of pixel-window size in evaluating building damage using the difference in the backscattering coefficient and correlation coefficient of pre- and postevent ERS images filtered for speckle noise reduction. From the above analysis an optimum window size for the damage evaluation was obtained. It was also found that the accuracy of damage detection was not significantly improved for specklereduction filtering of window size larger than 21×21 pixels.
This paper proposes determination of gravimetric deflections of the vertical derived from the gravimetric geoid“gsigeo2000”.Differences between the gravimetric and astronomic deflections are large in the mountainous areas, but are negligible in the flat and low land areas.The gravimetric deflections areuseful for the public survey of Japan to realize the projection method.
In most case of steep mountainside in Japan, slope failures are extending both sunny area and shadow area. To assess the actual disaster, accurate and reliable detection has to be required. Therefore the detection has to be able to estimate failures in the shade. In this study, a slope failure detector based on region growing algorithm is proposed for high-resolution satellite images. It calculates the magnitude and the morphology after the detection of failures in sunny area. The method was applied to detection of heavy rainfall induced slope failures in the upstream of the Yahagi River triggered by the Tokai Heavy rainfall, 2000. The conclusion is shown as follows: (1) Most of sliding failures can be detected precisely using a threshold value. (2) The shaded relief processing was effective to detect the debris flow failure along a valley, and the accuracy was improved.
The geometric accuracy of the Level lA product of Terra/MODIS has been investigated by comparing the contents of Latitude/Longitude file with GCPs. The investigation showed that the geometric accuracy by the systematic correction was a few kilometers on the ground. It is insufficient for land applications, especially for the use of 250m images. The system of the precise geometric correction based on the PANDA soft wares was developed. In the system, ‘bow-tie’effect, which is distinctive of MODIS, can be corrected and GCPs are extracted automatically by the template matching of image data and GMT coastal data. In the GCP extraction process, the template can be arrayed in either scanned image coordinates or map coordinates. And either the coastal lines or land/sea flag can be selected as the feature of the template. It was examined how efficient these selections are on the GCP extraction from 250m images. As the result, the system adopts the method of matching of land/sea flag in scanned image coordinates. Furthermore, the sensor alignment was modified using a number of GCPs. The modification improved the geometric accuracy of 250m images to about 1 pixel without GCPs.
Messy GA is applied to the satellite image clustering. Messy GA allows to maintain a long schema, due to the fact that schema can be expressed with a variable length of codes, so that more suitable cluster can be found in comparison to the existing Simple GA clustering. The results with simulation data show that tha proposed Messy GA based clustering shows four time better cluster separability in comparison to the Simple GA while the results with Landsat TM data of Saga show that almost 65% better clustring performance.