The spatial and temporal characteristics of the data used to describe moving objects' movement make them large in quantity and complex to manage. Different queries to motion data ask for various organization methods. According to the needs of most applications, general motion model is used to represent the translation and rotation of moving objects during a period of time. Because the motion data are multidimensional in space and time dimension, 2n tree is employed to construct the main part of the index to these data. Meanwhile other kinds of index algorithms should be added to the index structure so as to meet the needs of queries other than state queries only related to a specific epoch. Thus, motion data index structure (MDIS) is constructed as a multi-entry multi-level index structure for the organization of motion data. Each index within MDIS may work alone or cooperate with each other to process different kinds of queries. The extra space needed for MDIS is only about 5%~6% of the total storage space of motion data themselves. And the respond time to each query is much decreased and acceptable to most applications dealing with moving objects.
In the region covered by variable amounts of vegetation, pixel spectra received by remotely-sensed sensor with given spatial resolution are a mixture of soil and vegetation spectra, so vegetation covering on soil influences the accuracy of soils surveying by remote sensing. Mixed pixel spectra are described as a linear combination of endmember signature matrix with appropriate abundance fractions correspond to it in a linear mixture model. According to the principle of this model, abundance fractions of endmembers in every pixel were calculated using unsupervised fully constrained least squares(UFCLS) algorithm. Then the signature of vegetation correspond to its abundance fraction was eliminated, and other endmember signatures covered by vegetation were restituted by scaling their abundance fractions to sum the original pixel total and recalculating the model. After above processing, de-vegetated reflectance images were produced for soils surveying. The accuracies of paddy soils classified using these characteristic images were better than that of using the raw images, but the accuracies of zonal soils were inferior to the latter. Compared to many other image processing methods, such as K-T transformation and ratio bands, the linear spectral unmixing to removing vegetation produced slightly better overall accuracy of soil classification, so it was useful for soils surveying by remote sensing.