Abstract
Abstract. The application for GIS advances the ability of data analysis on remote sensing image. The classification and distill of remote sensing image is the primary information source for GIS in LUCC application. How to increase the accuracy of classification is an important content of remote sensing research. Adding features and researching new classification methods are the ways to improve accuracy of classification. Ant colony algorithm based on mode framework defined, agents of the algorithms in nature-inspired computation field can show a kind of uniform intelligent computation mode. It is applied in remote sensing image classification is a new method of preliminary swarm intelligence. Studying the applicability of ant colony algorithm based on more features and exploring the advantages and performance of ant colony algorithm are provided with very important significance. The study takes the outskirts of Fuzhou with complicated land use in Fujian Province as study area. The multi-source database which contains the integration of spectral information (TM1-5, TM7, NDVI, NDBI) and topography characters (DEM, Slope, Aspect) and textural information (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, Correlation) were built. Classification rules based different characters are discovered from the samples through ant colony algorithm and the classification test is performed based on these rules. At the same time, we compare with traditional maximum likelihood method, C4.5 algorithm and rough sets classifications for checking over the accuracies. The study showed that the accuracy of classification based on the ant colony algorithm is higher than other methods. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using remote sensing technology based on ant colony algorithm. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using remote sensing technology based on ant colony algorithm. The causes of LUCC have been analysed and some suggestions to the development of this region were proposed.
Highlights
The classification by extracting of remote sensing (RS) data is the primary information source for GIS in land resource application (Pei Tao, Zhou Chenghu, Han zhijun, Wang Min, Qin Chengzhi and Cai Qiang. 2001; Treitz P, Howarth P. 2000; Yu Ming, Ai Ting-Hua. 2007)
The land use and cover changes in Fuzhou for the near term is studied and display the figures by using remote sensing technology based on ant colony algorithm
The most commonly used traditional image classification methods based on spectrum, but the spectral based classification cannot obtain good result due to the spectral dimension shortage in spite of remote sensing image classification is an important means of extracting information
Summary
The classification by extracting of remote sensing (RS) data is the primary information source for GIS in land resource application (Pei Tao, Zhou Chenghu, Han zhijun, Wang Min, Qin Chengzhi and Cai Qiang. 2001; Treitz P , Howarth P. 2000; Yu Ming, Ai Ting-Hua. 2007). The classification by extracting of remote sensing (RS) data is the primary information source for GIS in land resource application 2005; Chen Shu-Peng, Tong Qing-Xi and Guo Hua-Dong. We must improve the classifying method to solve the uncertainty of classification. The methods on spatial data mining and knowledge discovery have be applied in some fields(Li De-ren, Wang Shu-liang, Li De-yi and Wang Xin_zhou. 2003) .Author has studied on C4.5 algorithm and rough sets and the combination of C4.5 algorithm and rough sets(Yu Ming, Ai Ting-Hua. 2009).The paper discussed remote sensing image data classification techniques based on ant colony algorithm under the support different variable and compared with the other algorithm.
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