A Multi-objective Approach for Building Hyperspectral Remote Sensed Image Classifier Combiners
Hyperspectral images are one of the most important data source for land cover analysis. These images encode information about the earth surface expressed in terms of spectral bands, allowing us to precisely classify and identify materials of interest. An approach that has been widely used is the combination of various classification methods in order to produce a more accurate thematic map based on classification of hyperspectral images. Our multi-objective remote sensed hyperspectral image classifier combiner (MORSHICC) approach uses a genetic algorithm-based strategy for choosing the best subset of classifiers, that is, the one which provides higher accuracy with the fewest possible amount of classifiers. We propose to use combiners that linearly weigh each classification approach through Genetic Algorithm (WLC-GA) and Integer Linear Programming (WLC-ILP). For building the combiners, we used three data representations and four learning algorithms, producing twelve classification approaches such that the multi-objective approach can select the best subset. Experimental results on well-known datasets show that the MORSHICC approach with WLC-GA and WLC-IP not only produces combiners with fewer classifier approaches but also improves the final accuracy rates. Therefore, these combiners may produce more accurate thematic maps for real and large datasets in a short time.
Unable to display preview. Download preview PDF.
- 5.Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. on Information Theory 13(1), 21–27 (January 1967)Google Scholar
- 6.Deb, K.: Multi-objective optimisation using evolutionary algorithms: an introduction. In: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, pp. 3–34. Wiley (2011)Google Scholar
- 7.Dos Santos, J.A., Faria, F.A., da S Torres, R., Rocha, A., Gosselin, P.H., Philipp-Foliguet, S., Falcao, A.: Descriptor correlation analysis for remote sensing image multi-scale classification. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3078–3081 (2012)Google Scholar
- 12.ILOG S.A.: CPLEX 12.5 User’s Manual (2012)Google Scholar
- 13.Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience (2004)Google Scholar
- 18.Prasad, S., Bruce, L.M., Chanussot, J.: Optical Remote Sensing: Advances in Signal Processing and Exploitation Techniques, vol. 3. Springer (2011)Google Scholar
- 20.Santos, A.B., de S. Celes, C.S.F., de A.Arajo, A., Menotti, D.: Feature selection for classification of remote sensed hyperspectral images: a filter approach using genetic algorithm and cluster validity. In: The 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), vol. 2, pp. 675–681 (2012)Google Scholar
- 21.Tinôco, S., Santos, A., Santos, H., dos Santos, J.A., Menotti, D.: Ensemble of classifiers for remote sensed hyperspectral land cover analysis: An approach based on linear programming and weighted linear combination. In: IEEE Int. Geoscience and Remote Sensing Symposium (IGARSS), pp. 4082–4085 (2013)Google Scholar