基于局部对比度和多向梯度的高光谱异常检测

武丽; 徐星臣; 王一安; 任佳红; 张嘉嘉; 赵东; 王新蕾

基于局部对比度和多向梯度的高光谱异常检测

武丽^{1, 2,},
徐星臣^{1, 2},
王一安³,
任佳红¹,
张嘉嘉⁴,
赵东^{1, 2},
王新蕾^{1, 2, ,}

1.
南京信息工程大学电子与信息工程学院, 江苏南京 210044
2.
无锡学院电子信息工程学院, 江苏无锡 214105
3.
西安石油大学电子工程学院, 陕西西安 710071
4.
西安电子科技大学物理学院, 陕西西安 710071

基金项目:

国家自然科学基金 2105258

江苏省自然科学基金 BK20210064

无锡市创新创业资金“太湖之光”科技攻关计划（基础研究）项目 K20221046

无锡学院人才启动基金 2021r007

详细信息

作者简介:
武丽（1983-），女，江苏东台人，硕士，副教授，硕导，主要研究方向为信号处理、模式识别，E-mail: wuli@cwxu.edu.cn

通讯作者:
王新蕾（1980-），女，山东潍坊人，博士，讲师，硕导，主要研究方向为机器学习、模式识别，E-mail: wangxinlei@cwxu.edu.cn

中图分类号: TP751.1
计量
- 文章访问数: 39
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- PDF下载量: 5
出版历程
- 收稿日期: 2024-04-27
- 修回日期: 2024-05-29
- 网络出版日期: 2025-05-27
- 刊出日期: 2025-05-19

Hyperspectral Anomaly Detection Based on Local Contrast and Multidirectional Gradients

WU Li^{1, 2,},
XU Xingchen^{1, 2},
WANG Yian³,
REN Jiahong¹,
ZHANG Jiajia⁴,
ZHAO Dong^{1, 2},
WANG Xinlei^{1, 2, ,}

1.
School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Electronics and Information Engineering, Wuxi University, Wuxi 214105, China
3.
School of Electronics and Information Engineering, Xi'an Shiyou University, Xi'an 710071, China
4.
School of Physics, Xi'an University of Electronic Science and Technology, Xi'an 710071, China

摘要

摘要:
为了充分利用高光谱图像的空间和光谱信息，并抑制图像中的噪声，提出了一种基于局部对比度和多向梯度的高光谱异常检测方法。首先，为利用局部光谱信息，提出了一种局部对比度策略，通过计算目标与背景之间的亮度差异，获得光谱检测得分图。然后，为了降低计算的复杂性，引入了一种光谱融合降维技术对高光谱图像进行处理。此外，提出了一种局部多向梯度特征方法，旨在减少图像噪声和保留局部细节特征，生成多向梯度检测得分图。最后，通过融合两张得分图，得到最终的异常结果图。实验结果表明，在4个经典数据集上本文方法能够成功展示异常目标，并且相较于其他7种方法，其检测精度更高、虚警率更低。
- 高光谱图像 /
- 异常检测 /
- 局部对比度 /
- 光谱融合降维 /
- 多向梯度特征
Abstract:
To fully utilize the spatial and spectral information of hyperspectral images and suppress image noise, a hyperspectral anomaly detection method based on local contrast and multidirectional gradient analysis is proposed. First, to leverage local spectral information, a local contrast strategy is introduced, generating a spectral detection score map based on the brightness difference between the target and the background. Then, to reduce computational complexity, a spectral fusion-based dimensionality reduction technique is proposed to process hyperspectral images. In addition, a local multidirectional gradient feature method is proposed to reduce image noise, retain local detail features, and generate a multidirectional gradient detection score map. Finally, the anomaly result map is obtained by fusing the spectral and gradient-based score graphs. Experimental results on four classical datasets demonstrate that the proposed method can successfully display abnormal targets in the result graph, achieving higher detection accuracy and lower false alarm rates compared to seven existing methods.
- hyperspectral image /
- anomaly detection /
- local contrast /
- spectral fusion dimensionality reduction /
- multi-directional gradient features

HTML全文

图 1 HLC-MDG方法的总流程图

Figure 1. Overall flow chart of the HLC-MDG method

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图 2 实验数据集的伪彩图和真实图

Figure 2. False color images and ground-truth images of the experimental data set

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图 3 不同参数对HLC-MDG方法性能的影响

Figure 3. Effect of different parameters on the performance of HLC-MDG method

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图 4 内外窗尺寸对HLC-MDG方法性能的影响

Figure 4. Effect of inner and outer window size on the performance of HLC-MDG method

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图 5 不同算法对4个数据集的检测结果

Figure 5. Detection results of different algorithms on four data sets

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图 6 四个数据集上不同算法的ROC曲线（P_D, P_F）

Figure 6. ROC curves of different algorithms on four data sets (P_D, P_F)

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图 7 四个数据集上不同算法的ROC曲线（P_D, τ）

Figure 7. ROC curves of different algorithms on four data sets (P_D, τ)

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图 8 四个数据集上不同算法的ROC曲线(P_F, τ)

Figure 8. ROC curves of different algorithms on four data sets (P_F, τ)

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表 1 四个数据集上不同算法的AUC_{(D, F)}值

Table 1 AUC_{(D, F)} values of different algorithms on four datasets

Dataset	Method
Dataset	GRX	LRX	LRASR	LSMAD	CRD	GTVLRR	AHMID	HLSC-MDGF
Gulfport	0.9525	0.9810	0.9563	0.9864	0.8862	0.9843	0.9857	0.9960
HYDICE	0.9931	0.9850	0.9788	0.9902	0.9811	0.9817	0.9770	0.9951
San Diego Ⅰ	0.9411	0.9748	0.9859	0.9830	0.9298	0.9943	0.9928	0.9991
San Diego Ⅱ	0.9832	0.9807	0.9661	0.9870	0.9788	0.9957	0.9845	0.9912

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表 2 四个数据集上不同算法的AUC_{(D, τ)}值

Table 2 AUC_{(D, τ)} values of different algorithms on four datasets

Dataset	Method
Dataset	GRX	LRX	LRASR	LSMAD	CRD	GTVLRR	AHMID	HLSC-MDGF
Gulfport	0.0736	0.1447	0.2174	0.3797	0.0093	0.4438	0.2436	0.3860
HYDICE	0.2189	0.3031	0.5314	0.2337	0.3384	0.2166	0.3987	0.4143
San Diego Ⅰ	0.1017	0.1670	0.5425	0.2141	0.0039	0.4536	0.4114	0.6080
San Diego Ⅱ	0.2889	0.0316	0.3007	0.2136	0.2333	0.4717	0.0657	0.2938

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表 3 四个数据集上不同算法的AUC_{(F, τ)}值

Table 3 AUC_{(F, τ)} values of different algorithms on four datasets

Dataset	Method
Dataset	GRX	LRX	LRASR	LSMAD	CRD	GTVLRR	AHMID	HLSC-MDGF
Gulfport	0.0248	0.0076	0.1113	0.0587	0.0063	0.0823	0.0317	0.0032
HYDICE	0.0331	0.0032	0.0466	0.0195	0.0420	0.0471	0.0106	0.0024
San Diego Ⅰ	0.0470	0.0338	0.0627	0.0404	0.0023	0.1263	0.0212	0.0037
San Diego Ⅱ	0.0714	0.0046	0.0855	0.0281	0.1110	0.0307	0.0059	0.0032

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表 4 HLC-MDG中有无LSC的AUC_{(D, F)}值

Table 4 AUC_{(D, F)} values of HLC-MDG with and without LSC

Method	AUC_{(D, F)}
Method	Gulfport	HYDICE	San Diego Ⅰ	San Diego Ⅱ
+LSC	0.9960	0.9951	0.9991	0.9912
-LSC	0.9688	0.9743	0.9803	0.9725

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表 5 SFRD和两种常用降维方法的AUC_{(D, F)}值

Table 5 AUC_{(D, F)} values of SFRD and two commonly used dimensionality reduction methods

Method	AUC_{(D, F)}
Method	Gulfport	HYDICE	San Diego Ⅰ	San Diego Ⅱ
SFRD	0.9960	0.9951	0.9991	0.9912
PCA	0.9885	0.9847	0.9922	0.9895
FS	0.9879	0.9920	0.9930	0.9866

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表 6 HLC-MDG中有无LMGC的AUC_{(D, F)}值

Table 6 AUC_{(D, F)} values of HLC-MDG with and without LMGC

Method	AUC_{(D, F)}
Method	Gulfport	HYDICE	San Diego Ⅰ	San Diego Ⅱ
+LMGC	0.9960	0.9951	0.9991	0.9912
-LMGC	0.9839	0.9843	0.9903	0.9852

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