联合目标特征引导与多重注意力的建筑物变化检测

doi:10.11947/j.AGCS.2024.20230436

摘要/Abstract

摘要：

高分遥感影像具有丰富的细节特征，建筑物变化类型多，尺度差异大。针对建筑物变化在复杂环境中易出现空洞和遗漏的问题，本文提出联合目标特征引导与多重注意力的建筑物变化检测方法，通过建筑物目标级引导强化类别信息，实现高分辨影像精细变化信息提取。该方法由建筑物显著增强模块和目标引导的多重注意力模块组成，通过全局深层特征感知与融合，提取建筑物重点区域，结合目标级特征引导和多重自注意力强化特征表达，增强上下文特征相关关系，有效减少细节特征损失，解决目标空洞和边缘不清晰造成的细节损失问题。通过两组试验表明该方法能够提升准确率，在变化种类越多的场景中有效减少变化损失，提高算法稳定性。

关键词: 高分遥感影像, 建筑物变化检测, 目标特征, 深度学习, 注意力机制

Abstract:

High-resolution remote sensing images have rich detail features, and building changes are of variable types with large scale differences. Aiming at the problem that building changes are prone to voids and omissions in complex environments, a building change detection method combining object feature guidance and multiple attention mechanism is proposed to realize fine change information extraction from high-resolution images by enhancing category information through building target-level guidance. The method consists of a building significant enhancement module and a target-guided multi-attention module, which extracts the key areas of the building through global deep feature perception and fusion, combines the target-level feature guidance and multiple self-attention to strengthen the feature expression, enhances the contextual feature correlation, effectively reduces the loss of detailed features, and solves the problem of loss of details caused by the target voids and unclear edges. It is shown through two sets of experiments that this method can improve the accuracy, effectively reduce the change loss in scenes with more kinds of changes, and improve the stability of the algorithm.

Key words: high-resolution remote sensing images, building change detection, object feature, deep learning, attention mechanism

中图分类号:

P237

丁少鹏, 卢秀山, 刘如飞, 杨懿, 顾海燕, 李海涛. 联合目标特征引导与多重注意力的建筑物变化检测[J]. 测绘学报, 2024, 53(6): 1224-1235.

Shaopeng DING, Xiushan LU, Rufei LIU, Yi YANG, Haiyan GU, Haitao LI. Building change detection method combining object feature guidance and multiple attention mechanism[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1224-1235.

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方法	P	R	F₁值	IoU	OA
FC-EF	78.23	82.28	80.20	66.95	97.93
FC-Siam-diff	88.73	85.90	87.29	77.45	98.73
FC-Siam-conc	86.43	85.93	86.18	75.72	98.60
STANet	91.38	85.01	88.08	78.69	98.74
BiT	89.56	90.33	89.94	81.72	98.98
ChangeFormer	88.62	91.78	90.17	82.10	99.02
SNAFF	91.94	89.65	90.78	83.11	99.07
GASNet	90.51	91.61	91.05	83.58	99.08
本文方法	91.82	91.34	91.58	84.47	99.14

方法	P	R	F₁值	IoU	OA
FC-EF	65.59	79.44	71.85	56.07	97.81
FC-Siam-diff	87.18	58.53	70.04	53.89	96.82
FC-Siam-conc	50.93	88.17	64.56	47.67	95.88
STANet	93.03	83.49	88.00	78.58	99.03
BiT	88.47	83.68	86.01	75.45	98.77
ChangeFormer	85.32	89.73	87.47	77.73	98.96
SNAFF	92.76	89.65	91.18	83.79	99.26
GASNet	92.83	90.33	91.56	84.44	99.29
本文方法	92.04	91.38	91.71	84.69	99.30

数据集	方法	P	R	F₁值	IoU	OA
LEVIR-CD	Baseline	88.04	89.90	88.96	80.11	98.86
	Baseline+BISEM	91.42	89.22	90.31	82.33	99.02
	Baseline+FGMAM	90.61	90.33	90.47	82.60	99.03
	本文方法	91.82	91.34	91.58	84.47	99.14
WHU-CD	Baseline	87.63	87.99	87.81	78.27	98.96
	Baseline+BISEM	91.61	88.13	89.83	81.55	99.15
	Baseline+FGMAM	87.47	91.89	89.63	81.21	99.09
	本文方法	92.04	91.38	91.71	84.69	99.30