Most high resolution remote sensing satellites use linear array push-broom sensors to take images. As the taken image is
composed of thousands of image lines which were sensed at different time instants, so if there was a moving object in the
scope of the scene, then geometric shape of the image of the object would be different from its image when it was still.
The difference is the deformation caused by the object's movement. This paper presented a method to calculate the
deformation and using the deformation to calculate the moving parameters of the moving object.
This paper presented a method for precisely computing the ground 2D size of any pixel in a satellite image received by a
push-broom linear array sensor, and further calculating the planar distance between any two pixels in the image. The
algorithm is deduced from the imaging principle of linear array sensors, with consideration of the arc of the earth's
surface, the height of ground, and the light refraction caused by the air. The author used the method to measure the
ground planar distance between two pixels in a Worldview-1 image, compared with the site measurement, the errors
were less than the size of pixel. As the computation is based on the instant position and orientation of the sensor, the
method is useful for local small area measuring and real time measuring.
At present, navigation data models, such as GDF4.0, KIWI, SDAL and WI 19134, didn't pay attention to form
pedestrian transport infrastructure into their models. With the development of navigation, pedestrian navigation has
become a hot topic. The research team put forward their pilot research on pedestrian data modeling for hybrid travel
patters, mainly including subway, bus and feet. Pedestrian road network modeling was made. Based on this, it carried out
the discussion on multi-level navigation data modeling of hybrid travel patterns. It also gave algorithm suggestion to
operate the optimal route computing more efficient. The future work is just to focus on demonstrate the algorithm.
Based on the image characteristics of Tianshan Mountains, using multi-temporal multi-band NOAA/AVHRR, MODIS
images, combined with high resolution CBERS-1/2 and ETM images, a model for estimating the area of snow cover and
the depth of snow cover at different places was proposed. The snow cover variation characteristics including the
distribution of snow cover, the depth of snow cover and the drawing method for snow cover were focused. Based on the
model, the snow cover of the area along Tianshan Highway from
1996-2006 was studied.
Snow hazard especially avalanche potential along G217 national highway in Tianshan Mountains using remote sensing
and GIS is evaluated and compared with actual site records of avalanche in a test area. Most places of the actual
avalanche accidents are consistent with the places with the high snow hazard potential. But there are several places of the
avalanche were not in the high hazard potential areas. The reason for this difference is discussed.