ABSTRACT

Floods occur frequently worldwide. The timely, accurate mapping of the flooded areas is an important task. Therefore, an unsupervised approach is proposed for automated flooded area mapping from bi-temporal Sentinel-2 multispectral images in this paper. First, spatial–spectral features of the images before and after the flood are extracted to construct the change magnitude image (CMI). Then, the certain flood pixels and non-flood pixels are obtained by performing uncertainty analysis on the CMI, which are considered reliable classification samples. Next, Generalized Regression Neural Network (GRNN) is used as the core classifier to generate the initial flood map. Finally, an easy-to-implement two-stage post-processing is proposed to reduce the mapping error of the initial flood map, and generate the final flood map. Different from other methods based on machine learning, GRNN is used as the classifier, but the proposed approach is automated and unsupervised because it uses samples automatically generated in uncertainty analysis for model training. Results of comparative experiments in the three sub-regions of the Poyang Lake Basin demonstrate the effectiveness and superiority of the proposed approach. Moreover, its superiority in dealing with uncertain pixels is further proven by comparing the classification accuracy of different methods on uncertain pixels.

摘要

洪水在世界范围内频繁发生。及时、准确地绘制被淹地区的地图是一项重要任务。因此，本文提出了一种基于双时相 Sentinel-2 多光谱图像的自动淹没区域映射的无监督方法。首先，提取洪水前后图像的空间光谱特征以构建变化幅度图像（CMI）。然后，通过对CMI进行不确定性分析，得到某些泛洪像素和非泛洪像素，这些像素被认为是可靠的分类样本。接下来，使用广义回归神经网络（GRNN）作为核心分类器来生成初始洪水图。最后，提出了一种易于实现的两阶段后处理，以减少初始洪水图的映射误差，并生成最终洪水图。与其他基于机器学习的方法不同，GRNN 被用作分类器，但所提出的方法是自动化和无监督的，因为它使用不确定性分析中自动生成的样本进行模型训练。在鄱阳湖流域三个分区的对比实验结果证明了所提出方法的有效性和优越性。此外，通过比较不同方法对不确定像素的分类精度，进一步证明了其在处理不确定像素方面的优越性。在鄱阳湖流域三个分区的对比实验结果证明了所提出方法的有效性和优越性。此外，通过比较不同方法对不确定像素的分类精度，进一步证明了其在处理不确定像素方面的优越性。在鄱阳湖流域三个分区的对比实验结果证明了所提出方法的有效性和优越性。此外，通过比较不同方法对不确定像素的分类精度，进一步证明了其在处理不确定像素方面的优越性。