首页|结合自适应形态属性剖面与决策融合的高分遥感变化检测

结合自适应形态属性剖面与决策融合的高分遥感变化检测

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随着对地观测技术的快速发展,高分遥感影像变化检测已成为遥感领域中的研究热点.空间分辨率的提高带来了丰富的空间信息,同时也导致了由于物候差异引起的实际属性并未发生变化,而光谱等表现特征发生变化的"伪变化"问题更为显著.形态属性剖面(Morphological Attri-bute Profiles,MAPs)作为一种高效空间信息建模方法,可从不同属性、多个尺度精细刻画复杂变化特征,已在变化检测领域任务中得到了广泛应用.尽管如此,已有MAPs方法通常未考虑差分剖面的属性及尺度平衡问题,从而容易陷入局部最优;同时,有效融合MAPs差分特征获取变化检测结果是此类方法面临的另一个难点.为此,研究提出了一种结合自适应形态属性剖面与决策融合的变化检测方法.首先,通过对MAPs进行CVA,提取初始差分特征集合;在此基础上,设计了一种综合考虑属性及尺度平衡性的择优目标函数(Balanced Optimal Objective Function,BOF),进而提取优化差分特征集合;最后,基于所提出的变化强度证据指标(Evidence Index,EVI)和证据置信度指标(Indicators Of Evidence Confidence,IOEC),构建了一种多特征决策融合框架获得变化检测结果.实验结果表明:所提出方法的总体精度(Overall Accuracy,OA)和F1分数(F1 score,F1)分别可达96.41%及88.67%以上,在目视分析和定量评价均显著优于对比方法,尤其针对本文所提"伪变化",该方法相较对比方法可实现更为精确的判别,可以有效缓解"伪变化".
High Resolution Remote Sensing Image Change Detection Combining Adaptive Morphological Attribute Profile and Decision Fusion
With the rapid development of earth observation technology,high-resolution remote sensing image change detection has become a research hotspot in the remote sensing domain.The increase in spatial resolution brings rich spatial information,but also leads to the problem of"pseudo-change"caused by the change of the spectrum and other performance characteristics,which does not change due to phenological differences.Morpho-logical Attribute Profiles(MAPs),as an efficient spatial information modeling method,can accurately describe complex change characteristics from different attributes and multiple scales,and have been widely used in the field of change detection tasks.Nevertheless,the existing MAPs methods usually do not consider the properties and scale balance of the differential profile,so they are prone to fall into local optimum;at the same time,the ef-fective fusion of differential features into change detection results is another difficult problem faced by such meth-ods.To this end,this paper proposes a change detection method that combines adaptive MAPs with decision fu-sion.Firstly,the initial differential feature set is extracted by CVA on the MAPs;On this basis,a Balanced Op-timal Objective Function(BOF)is designed to extract the optimal differential feature set;Finally,based on the proposed change intensity evidence index(EVI)and evidence confidence index(IOEC),a multi-feature deci-sion fusion framework is constructed to obtain change detection results.The experimental results show that the Overall Accuracy(OA)and F1 score(F1)of the proposed method can reach 96.41% and 88.67%,respective-ly.which are significantly better than the comparison methods in both visual analysis and quantitative evaluation.especially for the"pseudo-variation"proposed in this paper.Compared with the comparison method,the method in this paper can realize more accurate discrimination and effectively alleviate the"pseudo change".

High resolution remote sensing imageChange detectionMorphological attribute profileDecision fusion

谢涛、陈施施、瞿建华、王超

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南京信息工程大学 遥感与测绘工程学院,江苏 南京 210044

北京华云星地通科技有限公司,北京 100081

南京信息工程大学 电子与信息工程学院,江苏 南京 210044

高分辨率 变化检测 形态属性剖面 决策融合

国家重点研发计划项目国家自然科学基金项目江苏省自然资源发展专项资金(海洋科技创新)项目

2021YFC280330242176180JSZRHYKJ202114

2024

遥感技术与应用
中国科学院遥感联合中心

遥感技术与应用

CSTPCD北大核心
影响因子:0.961
ISSN:1004-0323
年,卷(期):2024.39(3)
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