摘要:
准确获取土地利用/覆盖(land use/land cover,LULC)信息,对区域空间规划和可持续发展具有重要指导意义。地表形态的复杂性、地物类型的多样性、遥感图像特征的非线性给传统的遥感图像分类方法带来了挑战,且传统方法未充分利用遥感图像所蕴含的丰富信息。文章发展了一种随机森林遥感图像分类方法,融合指数与主成分分量开展LULC信息提取。首先,选择研究区影像进行云量筛选、影像中值合成,得到年际遥感影像; 其次,计算多种指数,提取主成分分量,将其融入到遥感图像波段堆栈中; 然后,构建不同机器学习算法分类器; 最后,基于混淆矩阵,使用总体精度与Kappa系数对分类结果进行评估。在杭州湾区域的实验结果表明, 植被指数、水体指数、建筑物指数与主成分分量的辅助决策能够提高分类的准确性,总体精度和Kappa系数分别为91.42%和0.894 2,高于传统随机森林、分类回归树和支持向量机等方法。融合指数和主成分分量的遥感图像分类方法能够准确地提取遥感图像中的地表覆盖特征,得到高精度的土地利用分类结果,为地表精细分类提供方法支持。
Abstract:
Accurate information about land use/land cover (LULC) can provide significant guidance for regional spatial planning and sustainable development. However, conventional methods for remote sensing image classification are challenging due to complex surface morphologies, diverse surface feature types, and nonlinear features of remote sensing images. Therefore, they fail to fully utilize the rich information in remote sensing images. This study developed a random forest-based classification method for remote sensing images to extract LULC information by integrating indices and principal components. First, the images covering the study area were selected to determine cloud cover and conduct median synthesis of images, obtaining interannual remote sensing images. Then, various calculated indices and the extracted principal components were integrated into the band stacks of remote sensing images. Furthermore, classifiers were constructed using different machine-learning algorithms. Finally, based on a confusion matrix, the classification results were evaluated using overall accuracy and the Kappa coefficient. The experimental results of the Hangzhouwan area show that the decision support based on vegetation, water, building indices, and principal components can improve the classification accuracy, yielding overall accuracy and Kappa coefficient of 91.42% and 0.894 2, respectively, which were higher than those of conventional methods such as random forest, classification and regression tree, and support vector machine. The method for remote sensing image classification proposed in this study, which integrates indices and principal components, can obtain high-accuracy land use classification results by accurately extracting land cover features in remote sensing images. This study will provide method support for fine-scale surface classification.
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