Dynamic Classifier Selection Using Spectral-Spatial Information for Hyperspectral Image Classification



This paper presents a dynamic classifier selection approach for hyperspectral image classification, in which both spatial and spectral information are used to determine a pixel’s label once the remaining classified pixels’ neighborhood meets the threshold. For volumetric texture feature extraction, a volumetric gray level co-occurrence matrix is used; for spectral feature extraction, a minimum estimated abundance covariance-based band selection is used. Two hyperspectral remote sensing datasets, HYDICE Washington DC Mall and AVIRIS Indian Pines, are employed to evaluate the performance of the developed method. The classification accuracies of the two datasets are improved by 1.13% and 4.47%, respectively, compared with the traditional algorithms using spectral information. The experimental results demonstrate that the integration of spectral information with volumetric textural features can improve the classification performance for hyperspectral images.



Dynamic classifier selection, Hyperspectral image classification, Applied remote sensing


"This paper was partially supported by National Natural Science Foundation of China (Grant Nos. 41201341, 51379056), the Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (Grant No. 12R02), Key Laboratory of Satellite Mapping Technology and Application, National Administration of Surveying, Mapping and Geoinformation (KLSMTA-201301), and Key Laboratory of Advanced Engineering Surveying of National Administration of Surveying, Mapping and Geoinformation (Grant No. TJES1301)."


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