A complex spectral–spatial classification scheme for hyperspectral images is proposed and explored. The key feature of method is using widespread and simple enough algorithms while having high precision. The method combines the results of a pixel wise support vector machine classification and the segmentation map obtained by partitional clustering using majority voting. The k-means++ clusterization algorithm is used for image clustering. Principal component analysis is used to prevent redundant processing of similar data. The proposed method provides improved precision and speed of hyperspectral data classification.