In hyperspectral image (HSI) classification, using spatial information as a supplement to spectral information is an effective way to improve classification accuracy. In this article, a novel robust complementary method using spectral-spatial information is proposed to reduce the information loss in feature extraction, thus improving the classification effect. In short, two complementary feature extraction stages are used to get the probability maps for decision fusion. In the stage of pre-processing feature extraction, we propose an adaptive cubic total variational smoothing method (ACTVSP), which is first proposed and applied in the remote sensing research field, to obtain the first-stage probability map. At the same time, we utilize edge-preserving filtering in the post-processing stage and obtain the second probability map by means of pixel-level classifier. Finally, the probability-like maps obtained in the above two stages are integrated by decision fusion rules. Experiments on ten public datasets show the effectiveness of our proposed method and demonstrate the superiority of distinguishing different land covers on the basis of very few training samples. Therefore, it can be applied to practical applications in different scenes.

中文翻译：

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一种用于高光谱图像分类的互补光谱空间方法


在高光谱图像（HSI）分类中，利用空间信息作为光谱信息的补充是提高分类精度的有效途径。本文提出了一种利用光谱空间信息的鲁棒互补方法，以减少特征提取中的信息损失，从而提高分类效果。简而言之，使用两个互补的特征提取阶段来获取决策融合的概率图。在预处理特征提取阶段，我们提出了一种在遥感研究领域首次提出并应用的自适应三次全变分平滑方法（ACTVSP），以获得第一阶段概率图。同时，我们在后处理阶段利用边缘保留滤波，并通过像素级分类器获得第二概率图。最后，通过决策融合规则对上述两个阶段获得的类概率图进行整合。对十个公共数据集的实验表明了我们提出的方法的有效性，并证明了在很少的训练样本的基础上区分不同土地覆盖的优越性。因此，可以应用于不同场景的实际应用。