Title
High-Resolution Image Classification Using the Dynamic Differential Evolutionary Algorithm Optimized Multi-scale Kernel Support Vector Machine Method.
Abstract
With the fast development of remote sensing techniques, the spatial resolution of remote sensed image are improved significantly. However, the excessive spatial resolution leads to a sharp increase in data volume and spectral information confusion of objects. The multi-scale kernel learning (MSKL) method has shown an excellent advantage in classification of high-resolution satellite image. Nevertheless, the performance of the MSKL is dramatically influenced by the widths and weights of the Radial Basis Function (RBF) kernel, since its multi-scale kernel function is constructed by several RBF kernels. In order to achieve efficient multi-scale classifier, a new dynamic differential evolution (DE) algorithm is introduced in this paper. In addition, the spectral features and spatial fractal texture features of images are synthetically employed to construct the multi-scale kernel. The experimental results show that the multi-scale kernel based on the dynamic DE algorithm is superior to the traditional multi-scale kernel in obtaining a better multi-scale kernel classifier and with higher classification accuracy.
Year
Venue
Field
2018
BICS
Kernel (linear algebra),Radial basis function,Pattern recognition,Evolutionary algorithm,Computer science,Support vector machine,Differential evolution,Artificial intelligence,Contextual image classification,Image resolution,Kernel (statistics)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
14
5
Name
Order
Citations
PageRank
Xueqian Rong101.01
Aizhu Zhang2629.98
Genyun Sun314917.27
hui huang48417.04
Ping Ma5334.85