Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2/W1, 179-184, 2013
https://doi.org/10.5194/isprsarchives-XL-2-W1-179-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
14 May 2013
Study on Increasing the Accuracy of Classification Based on Ant Colony algorithm
M. Yu3,1, D.-W. Chen2, C.-Y. Dai1, and Z.-L. Li3,4 1College of Geographical Science, Fujian Normal University, Shangsan Road, Fuzhou, Fujian, China
2School of Sociology & Anthropology, Sun Yat-Sen University, Guangzhou, Guangdong, China
3Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong
4Faculty of Geosciences and Environmental Engineering, Southwest Jiao Tong University, China
Keywords: Remote Sensing Image, Increasing the Accuracy of Classification, Ant Colony Algorithm, LUCC 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.
Conference paper (PDF, 1111 KB)


Citation: Yu, M., Chen, D.-W., Dai, C.-Y., and Li, Z.-L.: Study on Increasing the Accuracy of Classification Based on Ant Colony algorithm, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2/W1, 179-184, https://doi.org/10.5194/isprsarchives-XL-2-W1-179-2013, 2013.

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