Remote sensing image classification based on optimized support vector machine

Liqian Cheng (Beifang University of Nationalities)
Wenxing Bao (Beifang University of Nationalities)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 12, No 2: February 2014

To resolve the problem of wetland remote sensing image classification, this paper presents an improved classification algorithm. In this algorithm, genetic algorithm (GA) selection, crossover operation is introduced to the standard particle swarm optimization algorithm (PSO) to form a hybrid particle swarm optimization algorithm (GAPSO). The hybrid algorithm can exploit the advantages of the genetic algorithm and particle swarm algorithm respectively to the full to obtain the global optimal parameters of support vector machine (SVM). Thus the wetland remote sensing image can be classified more accurately. Taking Ningxia Shahu wetland remote sensing images as an example, this paper makes a classification of wetland remote sensing images using optimized support vector machine, and the outcome of the experiment shows that this algorithm has better classification effect than that of similar algorithms. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.4325