CAJ | 학술논문

高光谱图像中存在着特征维度高而训练集小的问题。为解决该问题，提出了一种2步走的分类方法：1）通过支持向量机对图像进行初步分类，根据分类结果计算出每个类别的均值特征；2）使用1）计算出来的均值特征作为能量函数的数据项，然后利用图割原理对图像做二次分类。实验中发现：空间上相近的像素点往往具有相似的特征，且属于同一个类别。针对这种现象，提取一个将谱域特征和空域特征相结合的新特征。该特征既包含了光谱信息也包含了空间信息，具有较好的分类性能和鲁棒性。在Indian Pine数据集和Pavia University数据集进行实验，实验结果表明了本文提出方法的有效性。
고광보도상중존재착특정유도고이훈련집소적문제。위해결해문제，제출료일충2보주적분류방법：1）통과지지향량궤대도상진행초보분류，근거분류결과계산출매개유별적균치특정；2）사용1）계산출래적균치특정작위능량함수적수거항，연후이용도할원리대도상주이차분류。실험중발현：공간상상근적상소점왕왕구유상사적특정，차속우동일개유별。침대저충현상，제취일개장보역특정화공역특정상결합적신특정。해특정기포함료광보신식야포함료공간신식，구유교호적분류성능화로봉성。재Indian Pine수거집화Pavia University수거집진행실험，실험결과표명료본문제출방법적유효성。
The high?dimension of the feature vs. small?size of training set is an unsolved problem in the hyperspectral image classification task. To solve this problem a two?step classification method is proposed. Firstly, a preliminary classification is performed by the support vector machine ( SVM) and the classification results are used to calculate the mean feature ( MF) of each class. Secondly, a classification based on the graph cut theory is applied with the MFs as an input of the energy function. The experimental results showed that spatially nearby pixels have large possibilities of having the same label and similar features. Therefore, a new feature called spectral?spatial combination ( SSC) is extracted that combines the spectral?based feature and spatial?based feature. The SSC feature contains the related spectral and spatial information of each pixel and provides better classification performance and robustness. Experi?ment results on the Indian Pine dataset and the Pavia University dataset demonstrated the effectiveness of the pro?posed method.