A band selection model was described for efficient and accurate remotely-sensed vegetation mapping in cloudy mixed-vegetation areas, demonstrated with an application on mapping mangroves in Southeast Asia (SE Asia). We show how to use multi-source satellite imagery and Cloud Computing Platforms to improve mapping and computational efficiency in complex environments. A key element of the method relies upon field surveys to establish a detailed sample database that includes easily-confused land cover. The Maximal Separability and Information (MSI) model was developed to select key bands for target land cover classification from multiple satellite imagery based on two principles: 1. maximize separability of the target cover from other land cover; and 2. maximize and prioritize information from band combinations. Application of the MSI model to map mangroves in SE Asia using three optical and SAR data systems (Landsat OLI, Sentinel-2 and Sentinel-1) showed: 1. Sentinel-2 is better at classifying mangrove than Landsat and Sentinel-1; and 2. SWIR, NIR and Red bands (with SWIR in particular) are effective in separating mangrove from other vegetation. The MSI-mapped mangroves showed lower computation cost compared to using all bands from individual satellites, and higher accuracy (above 90%) when applied to SE Asia. It was robust in tolerating smaller sample sizes, thereby demonstrating computational feasibility and substantial improvements with the MSI model for large-scale land cover mapping in complex environments.

描述了一种带选择模型，该模型用于在混浊的植被覆盖地区进行高效，准确的遥感植被测绘，并在东南亚（东南亚）的红树林测绘中得到了证明。我们展示了如何使用多源卫星图像和云计算平台来改善复杂环境中的地图绘制和计算效率。该方法的关键要素是依靠实地调查来建立一个详细的样本数据库，其中包括容易混淆的土地覆被。开发了最大可分离性和信息（MSI）模型，基于两个原理，从多个卫星图像中选择了用于目标土地覆盖分类的关键波段：1.最大化目标覆盖与其他土地覆盖的可分离性；2.最大化和区分来自频段组合的信息。使用三个光学和SAR数据系统（Landsat OLI，Sentinel-2和Sentinel-1）将MSI模型应用于东南亚的红树林地图显示：1. Sentinel-2在分类红树林方面比Landsat和Sentinel-1更好；2. SWIR，NIR和Red波段（特别是SWIR）可有效地将红树林与其他植被区分开。与使用单个卫星的所有频段相比，MSI映射的红树林显示出更低的计算成本，并且应用于东南亚时，其准确性更高（超过90％）。它在容忍较小样本量方面具有强大的鲁棒性，从而证明了MSI模型在复杂环境中进行大规模土地覆盖制图的计算可行性和实质性改进。Sentinel-2在分类红树林方面比Landsat和Sentinel-1更好。2. SWIR，NIR和Red波段（特别是SWIR）可有效地将红树林与其他植被区分开。与使用单个卫星的所有频段相比，MSI映射的红树林显示出更低的计算成本，并且应用于东南亚时，其准确性更高（超过90％）。它在容忍较小样本量方面具有强大的鲁棒性，从而证明了MSI模型在复杂环境中进行大规模土地覆盖制图的计算可行性和实质性改进。Sentinel-2在分类红树林方面比Landsat和Sentinel-1更好。2. SWIR，NIR和Red波段（特别是SWIR）可有效地将红树林与其他植被区分开。与使用单个卫星的所有频段相比，MSI映射的红树林显示出更低的计算成本，并且应用于东南亚时，其准确性更高（超过90％）。它在容忍较小样本量方面具有强大的鲁棒性，从而证明了MSI模型在复杂环境中进行大规模土地覆盖制图的计算可行性和实质性改进。应用于东南亚时，精度更高（超过90％）。它在容忍较小样本量方面具有强大的鲁棒性，从而证明了MSI模型在复杂环境中进行大规模土地覆盖制图的计算可行性和实质性改进。应用于东南亚时，精度更高（超过90％）。它在容忍较小样本量方面具有强大的鲁棒性，从而证明了MSI模型在复杂环境中进行大规模土地覆盖制图的计算可行性和实质性改进。