Phase unwrapping is an important step for the phase shifting profilometry. The dual-frequency phase unwrapping method can unwrap the object with discontinues when the object is static by employing more fringe patterns. However, errors will occur when moving object is reconstructed. In this paper, a new phase unwrapping method with dual-frequency phase unwrapping method for the moving object measurement is proposed. The fringe pattern with low fringe pattern and high frequency are projected onto the moving object surface. Then, the phase values are retrieved for the two frequencies respectively. The relationship between the movement and phase value is analyzed and the phase variations caused by the movement is compensated. At last, the phase value is unwrapped by the traditional dual-frequency phase unwrapping method. The effectiveness of the proposed method is verified by simulations.
Phase retrieve is an important step for phase shifting profilometry (PSP). The existing phase retrieve methods can obtain the phase value successfully for static object. However, as multiple fringe patterns are required in PSP, when the object has movement, errors will be introduced. A new phase retrieve method for the object with 2D movement is proposed in this paper. The 2D movement is divided into translation movement and rotation movement. Then their influence on the phase value is analyzed and a new reconstruction model including the movement information is given. At last, the phase value is retrieved based on the new reconstruction model. The proposed method can eliminate the errors caused by 2D movement of object. The effectiveness of the proposed method is verified by simulations.
The Grand Canal of China is the longest ancient canal in the world. It is an astonishingly huge project in the history
of Chinese civilization. However, some sections have already disappeared as the development of society and change of
environment. It can be detected by using very high resolution image. Object-oriented method based on image
segmentation is being actively studied in the high resolution image process and interpretation to extract a variety of
thematic information. It includes two consecutive processes: first the image is subdivided into separated regions
according to the spectral and spatial heterogeneity in the image segmentation process and then the objects are assigned to
a specific class according to the class's detailed description in the image classification process. The result shows that the
object-oriented approach can realize the full potential of the very high resolution image, have higher accuracy compared
with traditional classification and allow quantitative analysis of land use, simplification of Remote Sensing and GIS