Different from the images of villages or towns where many homologous features can be extracted easily, the extraction of homologous features from multimode images located of sea islands is more difficult because the types of objects on sea islands are hard to recognize. When applying scale invariant feature transform (SIFT) to the image matching of sea islands, only sparsely matched couples with an uneven distribution are obtained. To resolve this problem, a new feature point matching method that combines a maximum similarity model and scale invariant feature transform (MS-SIFT) is proposed. This method can solve the primary difficulty of matching multimode images, which is to extract the massive feature points that are invariant to differences in spectrum, scale, and view angles between multimode images, and successfully obtain many precise matched couples of images located in areas with less remarkable features, especially for matching images of sea islands with less features. The application of the proposed algorithm shows that a large number of accurately matched couples could be identified. Additionally, the matched accuracy, space uniformity of the matched couples, and the running time computed based on the MS-SIFT and the traditional SIFT are analyzed and compared, which further demonstrate the effectiveness of the proposed algorithm.
KEYWORDS: Mining, Databases, Data mining, Data processing, Meteorology, Data centers, Geographic information systems, Analytical research, Geography, Process modeling
Spatial distribution pattern is an arrangement of two or more spatial objects according to some spatial relations, such as
spatial direction, topological and distance relations. In the real world, spatial objects and spatial distribution pattern all
vary continuously along the time-line. Traditional spatial and non-spatial data dissevers this continuous spatio-temporal
process. Under analyzing relations among spatial object, its attributes and spatial distribution pattern, we brought metaspatio-
temporal process, spatio-temporal process and spatial distribution pattern spatio-temporal process. Rainfall in
Eastern China has a typical spatial distribution pattern, being composed of the northern rain area and the southern rain
area. Through constructing spatio-temporal process transactions, the association rules can be extracted from spatiotemporal
process data set by the Apriori algorithm. The result of the spaio-temporal process association rule mining is
consistent with the analysis of the theory. Finally, it is concluded that the spatio-temporal process can describe change of
a spatial object in a defined time range, and change trend of one entity can be forecasted through varying trend of others
based on the valuable spatio-temporal process association rules.
With quick development of economy, spatial distribution and specialization level of China large scale commodity exchange markets whose turnover are more than 100 million Yuan, have changed greatly. And influencing factors which distribute in the research region have attribute information and spatial information and do not satisfy statistical independence. Commodity exchange market specialization index is brought forward to measure specialization degree, based on the former research and constrained co-local spatial association rule is used to analyze symbiotic pattern between specialization level and influencing factors. Constrained predicate templates and association rule templates can improve mining efficiency greatly. As the result shown, large scale commodity exchange market specialization level on
country-region spatial scale went down from 2000 to 2005 and rose at 2006. The interesting association rules extracted based on defined minimum support and confident can provide officers of region governments with rational advices on large scale commodity exchange markets planning and construction.
Coastal zone, as the interactive area between ocean and land, is a very complex and fragile system. Coastal zone supports
diverse and productive coastal ecosystems and provides significant ecological, cultural, and economic benefits to human
beings. However, with the rapid growth in both population and economy, coastal ecosystems are being subjected to ever
increasing pressure. Coastal zone that was formerly wisely used is being destroyed in the pursuit of economic wealth
which has greatly threaded human's sustainable development. Beside excessive resource exploitation and environmental
pollution, unreasonable land use is also another important fact which could cause great damages to the coastal
ecosystems. The purpose of this paper is to evaluate the degree of land exploitation and utilization in Zhuhai coastal zone
which locates at the west of Pearl River Estuary. A new model named Multidimensional-Vectors Model which is suitable
to coastal zone was established. Land-use data of Zhuhai coastal zone in 1995, 2000, and 2005 was used. The
exploitative intensity of land-use between 1995 to 2000, and 2000 to 2005 was calculated by this model. Main
characteristics of exploitative intensity of land in Zhuhai coastal zone can be generalized as follows: Areas with high
exploitative intensity is mainly concentrated alone the coastal line except some small area scattered in the region far
away from the sea. Area with high exploitative intensity from 1995 to 2000 is larger than that from 2000 to 2005, and the
spatial distribution of high exploitative intensity during 1995 to 2000 period is more concentrative than that form 2000 to
2005.
Conference Committee Involvement (1)
International Conference on Earth Observation Data Processing and Analysis
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