數位影像處理的過程可分五個部分，一、道路特徵之萃取，以分離衛星影像中道路面的可能資訊為目的，產生二元特徵影像；二、細部雜訊之消除，以降低特徵影像中的雜訊並進行特徵區塊編碼為目的，產生特徵區塊影像；三、道路特徵區塊之辨識，以辨識特徵區塊影像中的道路特徵區塊為目的，產生道路特徵區塊影像；四、道路特徵區塊形態之修正，以修正道路特徵區塊形態提供符合人類認知的道路資訊為目的，產生道路二元影像；五、道路交會口交點之搜尋，以輸出道路交會口交點位置為衛星影像之一個控制點為目的，產生一組控制點座標。
透過上述的數位影像處理步驟，在挑選高解析力衛星影像控制點時，便能夠降低人工介入的程度，迅速、確實的自動化萃取高解析力衛星影像控制點座標。

The high spatial resolution satellite images have widely attracted the attention in the remote sensing community recently. 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. The practicable experience indicates that the qualified image control points would be the points with striking features such as the road intersection. Thus, 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 aims 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 road intersection the main target to perform the control point extraction. The proposed image-processing algorithm consists of three steps. The first step is designed to segment the image and produce the feature image. The second step is proposed to extract roadblock features from the feature image. The third step is planned to locate the center position of the roadblock. A series of high spatial resolution satellite images are used to test the proposed method. The preliminary results shows that the proposed image processing approach has the potential to automatically position the control points in the high spatial resolution satellite image.

余松煜,周源華,吳時光,“數位影像處理”,儒林出版社,民國八十二年.
叢蕙茲,陳良健,“特徵匹配於衛星影像套合之應用”,航測及遙測學刊,第三卷第一期,第46－69頁, 民國八十七年
Chang, Y. L., Li, X., “Adaptive image region-growing”, IEEE Trans Image Process 3:868-872, 1994
Deok, J. P., Nam, K. M., Park, R. H., “Multi-resolution edge detection techniques”, Pattern Recognition, Vol. 28,no. 2, pp.211-229, 1995.
Doughetry, R. E., “An Introduction to Morphological Image Processing”, the Society for Optical Engineer Press, 1992
Geman, S., Geman, D., “Stochastic relaxation, Gibbs distributions, and Beyesian restoration of images”, IEEE Trans. on Pattern Anaylsis and Machine Intelligence, Vol. PAMI-6, no. 6, pp.721-741, 1984.
Geman, D., Jedynak, B., “An active testing model for tracking roads in satellite images”, IEEE Transacations on Pattern Analysis and Machine Intelligence, Vol. 18, no. 1, 98.1-14, 1996
Hassan, A.K., Xiaolong, D., Siamak, K., Aemal, J. K., Joseph, E. H., “Techniques for Automated Extraction of Roadway Inventory Features from High-Resolution Satelellite Imagery”, Geocarto Interational, Vol. 14. No 2, June 1999.
Parkar, J.R., “Algorithms for Image Processing and Computer Vision”, Wiley Computer Publishing, 1997.
Kim, T. H., Devarajan, V., Manry, M. T., “Road extraction from aerial images using neural networks”, Proceedings of ACSM/ASRPS, Vol. 3, Seattle, WA.pp.146-155, 1997.
Conzalez, C. R., Richard, E. W., “Digital Image Processing”, Addison-Wesley Publishing Company, 1993.
Wang, F., Newkirk, R., ”A knowledge-based system for highway network extraction”, IEEE Trans. on Geoscience and Remote Sensing, Vol. 26, no 5, pp.525-536, 1988.