基于上下文感知和超像素后处理的多光谱图像分类

高质量的地物类别提取是大量地学应用的基础。现有的基于像素的分类方法没有充分挖掘多光谱遥感图像中的上下文关联信息，且分类后的标签图像容易产生破碎。为了提升高分辨率遥感图像的分类精度，本文提出一种基于上下文感知网络和超像素后处理的多光谱图像分类方法。该方法利用新设计的卷积神经网络模型来更好地学习多光谱图像中的空间上下文信息。超像素后处理使用小区域分割和投票的策略来合并结构上关联的区域，以避免破碎标签的产生。本文方法在高分一号卫星数据上进行测试，并与6个分类算法进行比较。实验结果表明本文方法在精度和视觉效果上都优于比对算法。另外，对基于新模型分类后的结果进行超像素后处理，不仅减少了分类结果的破碎度，也进一步提升了图像的分类精度。

Multispectral Image Classification Based on Context-aware and Super-pixel Post-processing

To extract ground content is the basis for a large number of geoscientific applications. Existing pixel-based classification methods do not fully exploit the contextual associations in multispectral remote sensing images, and fragmented labels are observed everywhere in classified images. In order to improve the classification accuracy of high-resolution multispectral images, this paper proposes a new method which is based on context-aware networks and super-pixel post-processing. The method designs a new convolutional neural network to learn the spatial contextual information in multispectral images. Super-pixel post-processing uses a strategy of small region segmentation and voting to merge structurally associated regions, which can eliminate fragmented labels. The new method is tested on the Gaofen-1 satellite data and compared with six classification algorithms. The experimental results show that the new method outperforms the competing algorithms in terms of accuracy and visual effect. Among them, the super-pixel post-processing can reduce the fragmentation of classification results as well as improve the classification accuracy.