In recent years, the images captured by AVHRR (Advanced Very High Resolution Radiometer) on the NOAA (National Oceanic and Atmospheric Administration) series of satellites have been used very widely for environment and land cover monitoring. In order to use NOAA images, they need to be accurately transformed from the image coordinate system into map coordinate system. This paper proposes a geometric correction method that corrects the errors caused by this transformation. In this method, the errors in NOAA image are corrected in the image coordinate system before transforming into the map coordinate system. First, the elevation values, which are read from GTOPO30 database, are verified to divide data into flat and rough blocks. Next, in order to increase the number of GCPs (Ground Control Points), besides the GCPs in the database, more GCPs are generated based on the feature of the coastline. After using reference images to correct the missing lines and noise pixels in the top and bottom parts of the image, the elevation errors of the GCP templates are corrected and GCP template matching is applied to find the residual errors for the blocks that match GCP templates. Based on these blocks, the residual errors of other flat and rough blocks are calculated by affine and Radial Basis Function transform respectively. According to the residual errors, all pixels in the image are moved to their correct positions. Finally, data is transformed from image into map by bilinear interpolation. With the proposed method, the average values of the error after correction are smaller than 0.2 pixels on both latitude and longitude directions. This result proved that the proposed method is a highly accurate geometric correction method.

The percentage of pedestrian deaths in traffic accidents is on the rise in Japan. In recent years, there have been calls for measures to be introduced to protect vulnerable road users such as pedestrians and cyclists. In this study, a method to detect and track pedestrians using an in-vehicle camera is presented. We improve the technology of detecting pedestrians by using the highly accurate images obtained with a monocular camera. In the detection step, we employ ECoHOG as the feature descriptor; it accumulates the integrated gradient intensities. In the tracking step, we apply an effective motion model using optical flow and the proposed feature descriptor ECoHOG in a tracking-by-detection framework. These techniques were verified using images captured on real roads.