面向海岸带湿地高光谱遥感的欧拉映射与互补特征建模变化检测方法

doi:10.11947/j.AGCS.2026.20250409

测绘学报 ›› 2026, Vol. 55 ›› Issue (4): 618-631.doi: 10.11947/j.AGCS.2026.20250409

• 海岸带与海洋测绘遥感 • 上一篇

面向海岸带湿地高光谱遥感的欧拉映射与互补特征建模变化检测方法

吴岚昕¹^,²(), 彭江涛¹^,², 孙伟伟³^,⁴^,⁵(), 杨冰⁵^,⁶

^1.湖北大学数学与统计学学院，湖北　武汉　430062
^2.应用数学湖北省重点实验室，湖北　武汉　430062
^3.宁波大学地理科学与遥感技术学院，浙江　宁波　315211
^4.宁波市海岸带遥感与生态安全重点实验室，浙江　宁波　315211
^5.浙江-德国海岸带生态遥感联合实验室，浙江　宁波　315211
^6.中国计量大学理学院，浙江　杭州　310018

收稿日期:2025-09-30 修回日期:2026-03-13 发布日期:2026-05-11
通讯作者: 孙伟伟 E-mail:hubulanxinwu@163.com;sunweiwei@nbu.edu.cn
作者简介:吴岚昕（2000—），男，硕士，研究方向为海岸带高光谱遥感。　E-mail：hubulanxinwu@163.com
基金资助:
国家自然科学基金(42471417; 42471404; 62501557)

An Euler embedding and complementary feature modeling framework for hyperspectral change detection in coastal wetlands

Lanxin WU¹^,²(), Jiangtao PENG¹^,², Weiwei SUN³^,⁴^,⁵(), Bing YANG⁵^,⁶

^1.Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, China
^2.Hubei Key Laboratory of Applied Mathematics, Wuhan 430062, China
^3.School of Geographical Science and Remote Sensing Technology, Ningbo University, Ningbo 315211, China
^4.Ningbo Key Laboratory of Remote Sensing and Ecological Security of Coastal Zone, Ningbo 315211, China
^5.Zhejiang-Germany Joint Laboratory on Remote Sensing of Coastal Ecosystem, Ningbo 315211, China
^6.College of Sciences, China Jiliang University, Hangzhou 310018, China

Received:2025-09-30 Revised:2026-03-13 Published:2026-05-11
Contact: Weiwei SUN E-mail:hubulanxinwu@163.com;sunweiwei@nbu.edu.cn
About author:WU Lanxin (2000—), male, master, majors in coastal hyperspectral remote sensing. E-mail: hubulanxinwu@163.com
Supported by:
The National Natural Science Foundation of China(42471417; 42471404; 62501557)

摘要/Abstract

摘要：

海岸带湿地是维系生态平衡和应对气候变化的重要生态系统，其动态监测离不开遥感技术的支撑。凭借丰富的光谱信息，高光谱遥感能够准确识别湿地地物的细微差异，为生态系统保护与资源管理提供重要的数据支持。然而，现有基于卷积神经网络的高光谱变化检测方法受限于局部感受野，难以有效捕捉长距离依赖；而基于Transformer的方法虽然具备全局建模能力，但在变化检测任务中未能充分区分变化信息与不变信息，从而限制了检测性能。针对这一问题，本文提出了一种面向海岸带湿地高光谱遥感的欧拉映射与互补特征建模变化检测方法（EECFM）。该方法包含3个核心模块。首先，基于欧拉表示的空谱特征映射模块（ETSS）从水平、垂直和光谱通道3个维度联合建模，充分挖掘空间结构与光谱信息的互补性。然后，相似性增强模块（BSE）利用余弦相似度提取不变特征，并结合基于Scharr算子的差异注意力模块（SDA），通过Scharr算子在横纵方向捕捉精细差异，从而增强变化区域的判别能力。最后，本文设计了一种多阶段损失函数：初始阶段采用交叉熵以确保模型稳定收敛，后续阶段引入类别重加权的Focal损失并与交叉熵联合优化，从而提升对不同类别的稳健性并增强对变化区域的敏感性。在3类典型的湿地遥感数据集上的试验结果表明，EECFM在变化检测的精度与稳健性方面展现出了优越的性能，为复杂环境下的湿地动态监测提供了一条技术路径。

关键词: 海岸带湿地遥感, 变化检测, 高光谱遥感影像, Scharr算子, 互补特征建模

Abstract:

Coastal wetlands are vital ecosystems for maintaining ecological balance and mitigating climate change, and their dynamic monitoring relies heavily on remote sensing technologies. With abundant spectral information, hyperspectral remote sensing can accurately identify subtle differences in wetland surface features, providing essential data support for ecosystem protection and resource management. However, existing hyperspectral change detection methods based on convolutional neural networks are limited by their local receptive fields, making it difficult to capture long-range dependencies effectively. Although Transformer-based methods possess global modeling capability, they often fail to adequately distinguish between changed and unchanged information in change detection tasks, thereby constraining overall detection performance. To address this issue, this paper proposes an Euler embedding and complementary feature modeling method (EECFM) for hyperspectral change detection in coastal wetlands. The proposed method consists of three core modules. First, an Euler-transformation-based spatial-spectral feature extraction module (ETSS) jointly models images from horizontal, vertical, and spectral channel dimensions, fully exploiting the complementarity between spatial structures and spectral information. Second, a bi-temporal image similarity enhancement module (BSE) employs cosine similarity to extract invariant features, while a Scharr-based differential attention module (SDA) leverages Scharr operators in both horizontal and vertical directions to capture fine-grained differences, thereby improving the discriminability of change regions. Finally, a multi-stage loss function is designed: in the initial stage, cross-entropy is adopted to ensure stable convergence, and in the subsequent stage, a class-reweighted Focal loss is integrated with cross-entropy to enhance robustness to long-tailed classes and improve sensitivity to change regions. The experimental results on three representative wetland remote-sensing datasets demonstrate that EECFM delivers superior accuracy and robustness in change detection, providing a technical pathway for dynamic wetland monitoring under complex environmental conditions.

Key words: coastal wetland remote sensing, change detection, hyperspectral image, Scharr operator, complementary feature modeling

中图分类号:

P237

吴岚昕, 彭江涛, 孙伟伟, 杨冰. 面向海岸带湿地高光谱遥感的欧拉映射与互补特征建模变化检测方法[J]. 测绘学报, 2026, 55(4): 618-631.

Lanxin WU, Jiangtao PENG, Weiwei SUN, Bing YANG. An Euler embedding and complementary feature modeling framework for hyperspectral change detection in coastal wetlands[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(4): 618-631.

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数据集	图像尺寸/像素	通道数	采集时间		传感器
数据集	图像尺寸/像素	通道数	图像1	图像2	传感器
USA-Hermiston	307×241	154	2004-05-01	2007-05-08	Hyperion
HZB	400×400	166	2021-01-29	2022-02-27	ZY-02D
Yancheng	512×512	166	2022-02-24	2023-01-20	ZY-02D

方法	变化类别准确率							OA	AA	κ
方法	未变化	1	2	3	4	5	6	OA	AA	κ
Re3FCN	97.69±0.45	0.00±0.00	0.00±0.00	37.61±21.83	91.65±4.58	0.00±0.00	37.96±30.71	83.26±0.34	28.61±1.95	52.21±1.53
BIT	97.37±0.74	29.41±5.64	78.45±7.40	46.62±6.36	85.55±0.59	42.89±3.37	45.21±9.37	89.68±0.55	60.80±3.09	72.28±1.72
ConvLSTM	98.45±0.53	0.00±0.00	4.52±2.97	0.00±0.00	86.56±7.32	0.00±0.00	10.68±5.34	84.49±0.77	31.71±4.86	54.45±4.30
GLAFormer	96.96±0.51	22.23±5.91	69.28±2.96	30.32±8.85	78.91±5.03	38.21±5.50	33.82±7.30	87.38±0.69	52.82±2.09	66.14±1.79
BTCDNet	97.63±0.73	0.00±0.00	23.17±13.37	10.22±8.57	84.50±6.12	0.13±0.07	14.60±10.85	83.47±1.08	30.95±5.47	47.29±5.18
BT-SCD	96.91±1.07	42.19±11.91	57.81±7.65	38.68±5.54	82.14±4.27	34.27±12.47	51.35±5.72	88.38±1.19	57.63±0.92	68.77±2.39
EECFM	97.95±0.28	43.22±4.03	80.53±2.01	54.11±3.24	91.82±0.09	55.75±7.17	60.95±0.14	92.11±0.12	69.19±0.60	78.97±0.18

方法	变化类别准确率										OA	AA	κ
方法	未变化	1	2	3	4	5	6	7	8	9	OA	AA	κ
Re3FCN	98.42±0.41	0.00±0.00	0.00±0.00	0.00±0.00	0.00±0.00	63.76±3.24	11.48±8.15	0.00±0.00	91.58±0.95	0.00±0.00	89.90±0.13	26.92±1.65	68.63±0.43
BIT	98.64±0.08	96.09±2.71	100.00±0.00	50.19±8.08	63.52±1.10	76.20±3.28	61.81±4.36	64.17±12.79	85.84±2.82	49.47±12.57	93.84±0.47	74.60±2.97	81.27±1.64
ConvLSTM	98.63±0.58	0.00±0.00	0.00±0.00	0.00±0.00	0.00±0.00	65.92±19.48	0.00±0.00	0.00±0.00	94.85±1.19	0.00±0.00	90.19±1.09	25.94±1.93	68.51±5.32
GLAFormer	96.67±0.22	87.07±8.97	77.36±6.54	27.15±9.91	39.09±6.62	69.66±2.07	49.60±1.92	36.10±1.15	84.07±3.34	39.49±13.68	90.66±0.33	60.62±1.56	72.12±1.23
BTCDNet	98.05±0.22	55.77±8.40	64.78±6.11	0.00±0.00	0.73±0.42	64.69±13.38	2.59±1.50	32.76±19.48	84.36±13.21	27.30±5.46	89.47±1.33	41.64±2.34	65.02±6.22
BT-SCD	97.84±0.03	92.67±5.28	95.27±1.33	58.67±6.88	78.31±3.93	75.01±1.38	60.16±0.43	64.32±1.82	90.61±4.19	62.36±8.40	93.89±0.24	77.53±1.13	82.15±0.89
EECFM	98.86±0.14	85.37±4.87	93.08±8.91	61.28±6.04	74.92±4.21	80.83±3.23	70.96±6.61	72.59±5.33	93.58±2.45	68.50±12.31	95.62±0.19	82.72±5.85	86.94±0.58

方法	变化类别准确率							OA	AA	κ
方法	未变化	1	2	3	4	5	6	OA	AA	κ
Re3FCN	97.20±0.12	49.39±10.51	79.56±6.01	86.50±2.82	65.79±6.99	48.90±9.12	79.36±4.45	90.06±0.56	72.33±0.23	78.71±1.12
BIT	98.68±0.22	50.82±18.11	65.86±18.46	55.24±10.48	71.83±9.02	71.19±4.22	77.99±0.67	89.19±1.25	70.24±2.94	76.82±2.63
ConvLSTM	97.91±0.67	72.98±4.98	92.48±2.94	96.89±0.40	86.05±4.93	31.80±17.26	84.04±2.54	90.40±5.57	78.79±3.63	86.83±1.14
GLAFormer	97.14±0.39	66.69±5.18	82.85±3.51	90.97±2.43	82.02±1.82	71.10±0.21	78.61±1.40	92.14±0.32	81.34±1.27	83.29±0.78
BTCDNet	97.59±0.44	51.00±6.60	83.59±3.80	83.26±5.16	61.60±15.15	3.68±1.65	80.90±6.31	89.78±1.29	65.66±3.72	77.89±2.68
BT-SCD	98.08±0.25	67.93±2.17	83.06±2.13	95.30±0.49	90.21±3.24	80.63±1.65	77.79±1.22	93.27±0.03	84.72±0.76	86.01±0.17
EECFM	98.76±0.23	79.18±1.94	90.22±1.58	96.90±0.72	85.55±3.65	87.26±4.59	88.31±1.68	95.90±0.09	90.64±0.56	91.01±0.19

组别	模块			数据集
	模块			USA-Hermiston			HZB			Yancheng
	ETSS	BSE+SDA	L	OA	AA	κ	OA	AA	κ	OA	AA	κ
1	×	×	×	88.10	43.92	65.04	93.65	67.21	80.67	94.01	86.12	88.12
2	√	×	×	90.27	62.44	73.34	94.87	80.89	84.85	95.09	85.96	89.41
3	√	×	√	90.95	65.10	75.52	94.73	75.95	84.12	95.29	89.20	89.82
4	×	√	√	89.84	59.33	72.88	94.86	76.85	93.62	95.42	89.75	90.81
5	√	√	×	91.19	66.58	76.89	95.25	81.29	85.85	95.67	90.26	90.91
6	√	√	√	92.11	69.19	78.97	95.62	82.72	96.94	95.90	90.64	91.01

面向海岸带湿地高光谱遥感的欧拉映射与互补特征建模变化检测方法

An Euler embedding and complementary feature modeling framework for hyperspectral change detection in coastal wetlands

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图/表 15

参考文献 44

相关文章 15

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Metrics

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方法	FLOPs/亿次	参数量/万个
Re3FCN	8.91	28.94
BIT	134.53	1 644.58
ConvLSTM	44.18	27.69
GLAFormer	1 018.37	1 303.90
BTCDNet	40.29	157.50
BT-SCD	226.86	2 543.61
EECFM	17.58	70.05

算子	USA-Hermiston	HZB	Yancheng
Prewitt	91.55	95.04	95.52
Sobel	91.47	95.32	95.66
Scharr	92.11	95.62	95.90

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