基于鲁棒支持向量机的光谱解译

Spectral unmixing based on robust support vector machine

在传统基于线性光谱混合模型的光谱解译方法中,模型自身的不足以及在光谱解译过程中无关类别的参与都影响着解译效果。为此,提出一种基于支持向量机并结合空间信息的光谱解译方法。首先创建一种具有鲁棒特性的线性最小平方支持向量机(LLSSVM),并将其应用于初次光谱解译;然后利用空间信息进行智能性纯像素判定、解译结果校正,并对混合像素进行相关类别选择;最后再次应用LLSSVM在相关类别内进行二次解译。仿真试验中,本文方法比传统方法在解译精度上提高了10%,表明了该方法具有良好的解译效果。

In traditional spectral unmixing method based on linear spectral mixing model （LSMM）, unmixing accuracy is deteriorated both by the deficiency of the model and the participation of unrelated classes. Therefore, to solve this problem, a method with spatial information considered was proposed based on linear least square support vector machines （LLSSVM）. A robust LLSSVM was constructed and then applied to spectral unmixing. Then pure pixels were determined and unmixing results were corrected with related classes selection for each mixing pixel. Ultimately LLSSVM was used again for spectral unmixing with only related classes considered. Simulation results indicate that the unmixing accuracy of the proposed method is improved by 10 % higher than that of the traditional methods and is also high in efficiency.