摘要:
基于超像素分割的图像处理方法近年来被广泛应用于高光谱遥感图像(hyperspectral image,HSI)分类过程中,但是其单一尺度下无法充分提取HSI的丰富信息,且分类过程受参数依赖严重。因此针对基于超像素分割的HSI分类技术利用空间信息不足的问题,提出一种超像素分割方法和扩展多属性轮廓(extended multi-attribute profile,EMAP)方法相结合的HSI图像分类方法。该方法首先采用超像素分割方法提取超像素级特征,同时利用EMAP方法提取像素级HSI特征,融合2种特征后的图像具有完整的HSI结构特性,考虑到融合之后的信息冗余,采用递归滤波的方法进行光谱学滤波,最后将特征输入到支持向量机(support vector machine,SVM)分类器中,确定像素的标签。在Indian Pines和University of Pavia 这2个数据集上实验,分析了参数的变化对分类精度的影响,并与其他同类算法相比较,分类精度和Kappa系数较S3-PCA方法分别提高了3.55百分点和2.88百分点。
Abstract:
Superpixel segmentation-based image processing has been extensively used for the classification of hyperspectral images (HSI) in recent years. However, it fails to fully extract the HSI information at a single scale, and its classification process highly depends on parameters. Given the insufficient spatial information utilization by the superpixel segmentation-based HSI classification technology, this study proposed an HSI classification method that combines the superpixel segmentation method and the extended multi-attribute profile (EMAP) method. First, the superpixel segmentation and EMAP methods were employed to extract superpixel-level and pixel-level HSI features, respectively. By fusing the two types of features, the resulting images displayed complete HSI structural characteristics. To eliminate information redundancy, the fused images were subjected to spectral filtering through the recursive filtering method. Finally, the features were input to the support vector machine (SVM) for pixel tag determination. Experiments on the Indian Pines and University of Pavia datasets analyzed the effects of parameter variations on classification accuracy. Compared with the S3-PCA algorithm, the method proposed in this study exhibited superior classification accuracy and Kappa coefficient, which were improved by 3.55 and 2.88 percentage points, respectively.
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