Building detection from the satellite image is a computer vision, Photogrammetry, and remote sensing task that has significant importance in geographical information system (GIS) based applications. In this study, a novel framework is developed for the automatic detection of different types of buildings in the complex environment of the satellite images. The framework consists of fuzzy-based pre-segmentation, information extraction, and Grab-cut partitioning. The pre-segmentation and information extraction are employed to generate the initialization data for the Grab-cut method in an unsupervised manner. Further, the Grab-cut method partition the input image in building and non-building classes depending on the initialization data is provided. The performance of the proposed algorithm is evaluated over pan-sharpened satellite images having diverse built-up characteristics. The qualitative and quantitative assessments are conducted using standard statistical parameters. The proposed algorithm has achieved the average performance in terms of the F - score is 65%, and in terms of recall is 84%. Also, the proposed algorithm is compared with the existing state-of-the-art methods to illustrate the superiority and potential of the proposed algorithm over the existing ones.

从卫星图像进行建筑物检测是计算机视觉，摄影测量和遥感任务，在基于地理信息系统（GIS）的应用程序中具有重要意义。在这项研究中，开发了一种新颖的框架，用于在复杂的卫星图像环境中自动检测不同类型的建筑物。该框架包括基于模糊的预分段，信息提取和Grab-cut分区。预分段和信息提取用于以无监督的方式为Grab-cut方法生成初始化数据。此外，提供了Grab-cut方法，根据初始化数据将输入图像划分为建筑物和非建筑物类别。在具有各种组合特征的泛锐化卫星图像上评估了所提出算法的性能。使用标准统计参数进行定性和定量评估。提出的算法在F-得分方面达到了65％的平均性能，在召回方面达到了84％。此外，将所提出的算法与现有技术进行了比较，以说明所提出算法相对于现有算法的优越性和潜力。