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    国产多源卫片图斑智能提取平台研究与应用

    An intelligent platform for extracting patches from multisource domestic satellite images and its application

    • 摘要: 该文设计了一种基于深度学习框架的一站式国产多源卫片图斑自动提取平台。平台主要聚焦地物目标语义分割、图斑提取智能算法群和深度特征解译3个关键技术,针对遥感影像解译中色差较大、单张图像数据量巨大、多通道影像信息多元表达、不同遥感目标大小差距过大等问题,将智能语义分割和图斑自动提取算法群纳入系统框架内,构建了多种按需定制的通用模型及专题模型,同时开放模型自训练。平台集成海量数据管理、数据标注、模型训练、模型测试、图斑提取、应用分析等功能,实现了山西太原城区多源国产卫片建筑、植被、农田、工业区、水体等地物目标智能语义分割和图斑提取。

       

      Abstract: This study designed a one-stop platform for automatically extracting patches from multisource domestic satellite images based on a deep learning framework. The platform focuses primarily on critical techniques including semantic segmentation of ground objects, swarm intelligence algorithms for patch extraction, and deep feature interpretation. To address challenges in remote sensing image interpretation, such as significant color differences, vast data volumes of single images, diverse multi-channel image representations, and considerable differences in the sizes of remote sensing targets, the platform incorporates intelligent semantic segmentation and swarm intelligence algorithms for automatic patch extraction into the framework. It offers a range of customizable general and specialized models while supporting the self-training of models. With functions including large-scale data management, data annotation, model training, model testing, patch extraction, and application analysis, the platform has been successfully applied to the intelligent semantic segmentation and patch extraction of ground objects like buildings, vegetation, farmland, industrial zones, and water bodies in Taiyuan City, Shanxi Province based on multisource domestic satellite images.

       

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