基于独立分量分析和支持向量机的遥感影像融合分类算法

遥感影像分类是遥感定量化分析的重要手段,遥感影像融合是提高分类正确率的有效途径之一。本文提出一种遥感影像的融合分类算法。首先采用Contourlet变换对多光谱影像和全色影像进行融合,然后结合独立分量分析的去相关性、稀疏特性以及很好地捕捉影像重要边缘信息、纹理信息的能力,提取融合影像的独立分量特征,并用支持向量机实现分类。与其他算法的主、客观比较结果表明,该算法的实验效果较好,可有效地提高遥感影像的分类精度。

Classification of Remote Sensing Fused Image Based on Independent Component Analysis and Support Vector Machines

Remote sensing image classification is an important means for quantified remote sensing image analysis,and remote sensing image fusion can effectively improve the accuracy of image classification.This paper proposes a classification algorithm of remote sensing fused image based on independent component analysis(ICA) and support vector machines(SVMs).Firstly a novel method of fusing panchromatic and multispectral remote sensing images is developed by contourlet transform which can offer a much richer set of directions and shapes than wavelet.As independent component analysis can not only effectively remove the correlation of multispectral images,but also realize sparse coding of images and capture the essential edge structures and textures of images.Then using features extracted from the ICA domain coefficients of the fused image,the SVMs are trained to classify the whole fused image.Experimental results show that the proposed algorithm can effectively improve the accuracy of the image classification.