自然资源遥感智能解译中复杂场景样本抽样方法研究  

Sampling Method for Complex Scene Samples in the Intelligent Interpretation of Natural Resources Remote Sensing

作　　者：李连发[1,2] 高茜琳 贺伟 陈淼淼 杨晓梅 王志华[1,2] 张俊瑶 刘晓亮[1,2] LI Lianfa;GAO Xilin;HE Wei;CHEN Miaomiao;YANG Xiaomei;WANG Zhihua;ZHANG Junyao;LIU Xiaoliang(State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院地理科学与资源研究所地理信息科学与技术全国重点实验室,北京100101 [2]中国科学院大学,北京100049

出　　处：《地球信息科学学报》2025年第2期331-349,共19页Journal of Geo-information Science

基　　金：国家重点研发计划项目(2021YFB3900501);中国科学院战略性先导科技专项资助(XDB0740100);国家自然科学基金项目(42071369、42471500)。

摘　　要：【目的】随着遥感分类解译技术的发展,复杂场景下自然资源遥感智能解译已成为研究的焦点。在这一背景下,遥感样本的获取和选择对提高解译的准确性和可靠性至关重要。我国地形地貌多样,气象条件复杂,地表结构细碎,导致复杂地表具有时空分异性,直接影响遥感样本的选择和质量。传统抽样方法所选样本对总体特征的代表性较差,进而影响解译效果。【方法】为解决这一问题,本研究总结了遥感分类标记样本抽样方法、多尺度形态转换扩充样本以及标记样本质量评估的关键要点,从理论上阐述了样本优选以减少偏差的必要性,并提出了基于地表复杂度的分区/层和加权样本优化方法。该方法通过考虑地形的复杂性和多样性,优化了样本的抽样过程,减少了因抽样偏差带来的解译误差。【结果】通过遵循这些要点和技术,可以获得高质量且有强代表性的标记样本,从而提高遥感分类建模解译精度和/或效益。本研究总结了3个基于复杂度的样本优选的实验结果,并为未来遥感智能解译技术的发展提供了坚实的理论和技术基础。【结论】这项研究对于通过样本优选推动复杂场景下遥感自然资源分类建模及智能解译的研究和实际应用具有重要的参考价值。[Objectives]As remote sensing classification and interpretation technologies continue to advance,the intelligent interpretation of natural resources in complex environments has become a critical research focus.The accuracy and reliability of remote sensing data interpretation depend fundamentally on the quality and representativeness of the samples used in the analysis.In China,diverse terrain,complex meteorological conditions,and fragmented land surface structures introduce significant spatiotemporal variability,making the selection and quality of remote sensing samples particularly challenging.Traditional sampling methods often fail to adequately represent the full spectrum of characteristics inherent in these diverse landscapes,leading to substantial biases and inaccuracies in interpretation outcomes.[Methods]To address these challenges,this study offers a comprehensive review of key elements in remote sensing classification,encompassing methods for sampling labeled data,techniques for multi-scale morphological transformations to augment samples,and strategies for evaluating the quality of labeled samples.The research emphasizes the critical importance of optimizing sample selection to reduce bias and improve interpretation accuracy.It explores the theoretical foundations for sample optimization,highlighting the necessity of obtaining representative samples that accurately capture the complexity and variability of the land surface.[Results]One of the primary contributions of this study is the development of a novel sampling optimization method that integrates terrain complexity into the sampling process.By considering the diverse and intricate nature of the landscape,our approach enhances the representativeness of the samples,thereby reducing errors introduced by sampling bias and significantly improving the accuracy of remote sensing interpretation.In particular,we emphasize the role of multi-scale morphological transformations,which allow for the expansion of sample diversity and the generation of more robus

关 键 词：遥感智能解译 自然资源 优化样本抽样设计 形态转换 复杂场景 

分 类 号：P237[天文地球—摄影测量与遥感] P962[天文地球—测绘科学与技术]