基于遗传算法的混合像元快速分解及分类算法

在深入研究混合像元分解原理的基础上,提出了用遗传算法进化模型拟合分解结果的超平面,以实现混合像元分解并进一步分类的算法。给出了用该方法对多光谱图像中混合地物进行分类的实例。实验结果证明:该算法的结果与全约束最小二乘(FCLS)的混合像元分解算法结果相近(相关系数达到0.99),而计算复杂度大大降低,计算速度明显提高且具有较强的适应性。同时在整幅遥感图像的分类中体现出较高的分类性能,为遗传算法在混合地物分类问题中的应用提供了又一条可行的途径。

Mixed Pixel Unmixing and Classification with Genetic Algorithm

By researching the principles of the mixed pixel unmixing algorithm,this paper proposes a new GA-based method for mixed pixel unmixing and classification problems,which uses the GA evolution models to fit(approximate) the hyperplane of the unmixing results.The validity and feasibility of the algorithm are demonstrated by remote multispectral images.Experimental results show that the approach is able to get almost the same category proportion with the method of Full Constrained Least-squares(correlation coefficients are above 0.99),and has more flexibility than the method of least squares.