As IKONOS satellite with 1-m resolution has been lunched in 1999, mapping using space-borne images will be a hot issue in computer vision area and photogrammetry. It is obvious that one of the great challenges to process the high spatial resolution satellite images will be the geometric correction practice. Conventionally, the positioning of the image control points is manually performed by a labor-intensive and time-consuming procedure. Furthermore, due to the abundant image contents, high spatial resolution satellite image would have plenty of the qualified control points. As a result, the manual identification and positioning of control points will become even more inefficient and unbearable. Therefore, the main objective of this study proposes to develop an automated image processing technique to extract the control points for the high spatial resolution satellite images. Among numerous spatial features, this study considers even widespread road intersection the main target to perform the control point extraction. The proposed method consists of two parts. The first part is "road extraction" consisting of four steps image-processing algorithm and the second part is "road intersection searching" consisting of two steps image-processing algorithm. A series of high spatial resolution satellite images are used to test the proposed method. The results shows that the oposed image processing approach has the potential to automatically position the control points in the high spatial resolution satellite image.

We used the observed dinoflagellate densities as ground truth data and selected the training regions with the ground truth data then analyzed the dinoflagellate (Peridinium bipes) algal bloom areas in the Techi reservoir using mahalanobis classification method. The results of Mahalanobis classified images and ground truth data are similar. They all showed that algal bloom areas most located on the upstream of the Techi reservoir. The algal bloom sites and areas varied with seasons because many agricultural activities on the upstream of Techi reservoir and fertilizers ran off to the reservoir which provided a good environment for the growth of Peridinium bipes. The reason why the algal bloom site and areas varied with seasons is because that the fertilized period and fertilized quantities varied with the different growth period of vegetation, and as the results, fertilized quantities ran off to the reservoir varied with different seasons. In conclusion, Mahalanobis classification method is a powerful tool for monitoring algal blooms, by getting entire water body data and temporal water body data simultaneously.

One of the challenging problems in making absolute ionospheric delay measurements using the dual-frequency observations to the GPS satellites is to estimate satellite L1/L2 differential delay (BS) and receiver Ll/L2 differential delay (BR). In this paper an algorithm is proposed which can estimate the receiver L1/L2 differential delay (BR) of any static GPS site. Then, the accumulated values of the estimated BR and the satellite L1/L2 differential delays (BSs) estimated by JPL, SPRs, are used to correct those GPS derived TEC estimates. The estimation method and preliminary test results are described here. GPS Data from sites CUA1 and CUA2, National Chengchi University, Taipei, Taiwan were used to test the proposed algorithm. The test results indicate that: (1) The estimated receiver L1/L2 differential delays of CUA1 and CUA2 are 4.73 ns and 5.67 ns respectively using year 2002 data. (2) The maximum and minimum VTEC values observed at site CUA2 on 2002/03/03 are 60.7 ns and 2.7 ns respectively, after applying estimated SPR corrections. (3) The mean value and standard deviation of the VTEC differences to those common GPS satellites from sites CUA1 and CUA2, on 2002/03/03, are 0.34 ns and 0.10 ns respectively. (4) The mean value and standard deviation of the differences between the observed SPR and the estimated SPR using the proposed algorithm of site CRFP, PGGA, are 0.13 ns and 0.98 ns respectively.

Using a flexible and efficient way to obtain aerial images has been the primary purpose of this study. The balloon platform was used to take aerial images. A video camera and a digital camera were fixed together in a durable plastic box, and hung on the balloon. The video camera was used to monitor the ground view, and its image could be telemetered remotely and displayed on a LCD monitor arranged on the ground. Once monitoring the area of interest shown on the LCD, the shutter button of the digital camera was then pushed remotely and the interested image was taken. The resultant images were ortho-rectified for analysis and comparison. The accuracy of aerial images was examined by check points. The results showed that the images achieved sub-pixel accuracy and were well-matched with the 1/1000 digital topographic maps. This expressed that it was really a useful and efficient method of taking large-scale images for a small research area. At last, post-classification comparison method was introduced to detect change of the ortho-rectified images which were taken in three different periods. The classification maps and the from-to change class information clearly indicated the change of river way among various periods.

For a digital stereo mapping system, there are several different methods to obtain stereoscopic vision. A photogrammetric system based on a desktop personal computer and a mount-on mirror stereoscope is utilized and evaluated for its functionality and mapping environment in this study. It is found that this system can provide basic mapping functions such as handling interior, relative, and absolute orientations, and maintains the perspective relation of the stereo-pair during the mapping process. Mapping accuracy to less than 2 pixels in XY-plane and 4 pixels in height can be achieved for plain operators.

Any model built by the raw remote sensing data cannot be used for prediction, since the signal, which is reflected or thermally emitted from the surface, received by the sensor is absorbed or scattered by the atmosphere. Therefore, it's indispensable that raw image be corrected for atmospheric effect for the purpose of remotely sensing surface reflectance. In order to quickly process multi-temporal data for land-cover monitoring, we develop a window-based atmospheric correction information system for remotely sensed data, since the model requires too many parameters for input. The atmospheric correction information system includes both modules of graphic user interface and BACM satellite image processing. Its advantages include user friendly GUI, automation of input of parameters for atmospheric correction, simple functions of image display and enhancement and independence of BACM with GUI module for convenience of updating improved model. By using the limited data measured by spectroradiometer, the results show that RMSE of rice reflectance has been reduced from 0.089 to 0.04 and that of NDVI has been also reduced from 0.418 to 0.164 after atmospheric correction for case study of using SPOT HRV image to retrieve surface reflectance. Hence, BACM can effectively reduce the atmospheric effect of remotely sensed data. Nevertheless, limitations and future approaches of improvement of the system have also been discussed.