Abstract. An increasing number of applications require land cover information from remote sensing images, thereby resulting in an urgent demand for automatic land use and land cover classification. Therefore, effectively improving the accuracy of land cover classification is a main objective in remote sensing image processing. We propose a land cover classification postprocessing framework based on iterative self-adaptive superpixel segmentation (LCPP-ISSS) for remote sensing image data. This framework can further optimize the land cover classification results obtained by neural networks without changing the network structure. First, we propose the iterative self-adaptive superpixel segmentation algorithm for high-resolution remote sensing images to extract the boundary information of different land cover classes. Then, we propose a land cover classification result optimization method based on patch complexity to optimize the classification result by combining the boundary information with the semantic information. In an experiment, we compare the classification accuracy before and after using LCPP-ISSS and with other common methods. The results show that LCPP-ISSS outperforms the dense conditional random field and provides a 4% increase in the mean intersection over union and a 10% increase in overall accuracy.

中文翻译：

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基于迭代自适应超像素分割的土地覆盖分类优化后处理框架

摘要。越来越多的应用需要从遥感图像中获取土地覆盖信息，从而迫切需要自动土地利用和土地覆盖分类。因此，有效提高土地覆盖分类的精度是遥感图像处理的主要目标。我们提出了一种基于迭代自适应超像素分割（LCPP-ISSS）的土地覆盖分类后处理框架，用于遥感图像数据。该框架可以在不改变网络结构的情况下，进一步优化神经网络得到的土地覆盖分类结果。首先，我们提出了高分辨率遥感图像的迭代自适应超像素分割算法，以提取不同土地覆盖类别的边界信息。然后，我们提出了一种基于补丁复杂度的土地覆盖分类结果优化方法，通过结合边界信息和语义信息来优化分类结果。在一个实验中，我们比较了使用 LCPP-ISSS 和其他常用方法前后的分类精度。结果表明，LCPP-ISSS 的性能优于密集条件随机场，并且平均交集比并集提高了 4%，整体精度提高了 10%。