Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/88915
Title: Study on increasing the accuracy of classification based on ant colony algorithm
Authors: Yu, M 
Chen, DW
Dai, CY
Li, ZL 
Issue Date: 14-May-2013
Source: International archives of the photogrammetry, remote sensing and spatial information sciences, 14 May 2013, v. XL-2/W1, p. 179-184
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.
Keywords: Remote sensing image
Increasing the accuracy of classification
Ant colony algorithm
LUCC
Publisher: Copernicus GmbH
Journal: International archives of the photogrammetry, remote sensing and spatial information sciences 
ISSN: 1682-1750
EISSN: 2194-9034
DOI: 10.5194/isprsarchives-XL-2-W1-179-2013
Description: 8th International Symposium on Spatial Data Quality, May 30-Jun 01, 2013, Hong Kong
Rights: © Author(s) 2013. This work is distributed under the Creative Commons Attribution 3.0 License (https://creativecommons.org/licenses/by/3.0/).
The following publication 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, 2013 is available at https://dx.doi.org/10.5194/isprsarchives-XL-2-W1-179-2013
Appears in Collections:Conference Paper

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