Abstract

Land use/cover information is fundamental for the sustainable management of resources. Notwithstanding the advancement of remote sensing, analysts daunt to generate sufficient-quality land use/cover products due to dense-cloud-contaminated and/or technical issues. This study proposes a novel approach (Ensemble Learning Updating Classifier/ELUC), which can be applied with various classification algorithms and data sets to simplistically generate new classifications or renew existing classifications with a remarkable accuracy improvement. Applying miscellaneous features of Landsat-8 images, the ELUC of a random-forest-based algorithm produces sequences of single-time classifications with a mean overall accuracy of 84%. Through the study period, these sequences of individual classifications were then joined to achieve a final classification which reaches an overall accuracy of 94%. Also, the ELUC of the random-forest-based algorithm outperforms that of Kernel-Density-Estimation with a 5% overall accuracy higher. These outcomes confirm the effectiveness of the ELUC for a remarkably consistent land use/cover estimation with a data-rich environment.

摘要

土地使用/覆盖信息对于资源的可持续管理至关重要。尽管遥感技术取得了进步，但由于密云污染和/或技术问题，分析人员难以产生足够质量的土地使用/覆盖产品。这项研究提出了一种新颖的方法（集成学习更新分类器/ ELUC），该方法可以与各种分类算法和数据集一起使用，以简单地生成新分类或更新现有分类，从而显着提高准确性。利用Landsat-8图像的其他特征，基于随机森林算法的ELUC产生单次分类序列，平均总体准确度为84％。在整个学习期间 然后将这些单独分类的序列进行合并，以实现最终分类，最终分类的准确度达到94％。同样，基于随机森林的算法的ELUC优于内核密度估计的ELUC，其总体精度要高5％。这些结果证实了ELUC在具有丰富数据的环境下进行土地使用/覆盖率估算的显着一致性的有效性。