首页|基于空谱多路自编码器的高光谱图像异常检测

基于空谱多路自编码器的高光谱图像异常检测

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  • 维普
高光谱图像异常检测作为一种无监督的目标检测,主要存在异常目标类型多样化、异常与背景不易区分、以及检测精度受场景影响大等难题.针对以上问题,本文提出了一种基于空谱多路自编码器的高光谱图像异常检测方法.首先,提出一种加权空谱Gabor滤波方法,提取高光谱图像的多尺度空谱特征;其次,采用多路自编码器降低多尺度空谱特征在光谱维的冗余度,提取空谱特征中的主要信息;最后,利用得到的主要空谱特征,结合形态学滤波与双曲正切函数进行特征增强,以提高异常与背景噪声的区分度.本文提出的方法是一种即插即用的异常检测方法,无需额外的参数输入;多路自编码器提取了多尺度主要空谱特征,以应对异常目标类型多样化的难题;通过特征增强提高了背景与异常的区分度.将本文提出的方法与9种流行的异常检测方法相比,在5个高光谱数据集上进行验证,通过对比异常检测结果图、接收机操作特性(Receiver Operating Characteristic,ROC)曲线、ROC曲线下覆盖的面积AUC(Area Under Curve)以及异常像元与背景像元的箱型图等评价指标,证明了本文方法优于其他9种方法.
Hyperspectral anomaly detection based on spatial-spectral multichannel autoencoders
Hyperspectral anomaly detection is a type of unsupervised target detection that is crucial in the national economy and attracts the attention of numerous researchers.However,hyperspectral anomaly detection faces several challenges,such as diversified anomaly targets,difficulty in distinguishing anomalies from the background,and low detection accuracy.A hyperspectral anomaly detection method based on multichannel autoencoders is proposed to form a high-dimensional spatial-spectral feature space to address the above challenges.First,weighted spatial-spectral Gabor kernels with different scales and directions are proposed to extract the spatial-spectral features from hyperspectral images.These Gabor kernels are then redefined to increase the gap between the central and surrounding values in the kernels.The spatial-spectral features are extracted by weighted spatial-spectral Gabor kernels to form a high-dimensional spatial-spectral feature space.Second,multichannel autoencoders reduce the redundancy of multiscale spatial-spectral features in spectral dimension,extract the principal features from high-dimensional spatial-spectral feature space,and transform them into the principal feature representation space.Finally,a feature enhancement method based on hyperbolic tangent function and morphological filters is proposed to improve the distinction between abnormal targets and background noise and address the background noise in the spatial dimension.Mahalanobis distance is used to detect anomalies in the enhanced principal feature representation space.The proposed method is compared with nine state-of-the-art anomaly detection methods on five hyperspectral data sets.Anomaly Detection Maps(ADMs),Receiver Operating Characteristics(ROCs),Area Under Curves(AUCs),and box plots between abnormal and background pixels are used to evaluate the performance of the compared methods.AUC is a quantitative evaluation method,and the others are qualitative evaluation methods.The anomaly detection maps obtained by the proposed method easily locates abnormal targets compared with other methods.The ROC curves on five hyperspectral data sets show that the proposed method has a superior performance.The AUC values of five hyperspectral data sets are 0.9910,0.9912,0.9968,0.9806,and 0.9812.The box plots show that the proposed method increases the gap between the anomalies and the background.The ablation experiments show that weighted spatial-spectral Gabor can extract more significant spatial-spectral features than three-dimensional Gabor.The principal feature representation space obtained by multichannel autoencoders is highly conducive to hyperspectral anomaly detection and improves detection accuracy.The feature enhancement method based on hyperbolic tangent function can improve the distinction between abnormal targets and background noise.The proposed method can extract significant spatial-spectral features from hyperspectral images to address the diversification of anomaly types and form a high-dimensional spatial-spectral feature space.The multichannel autoencoders convert the high-dimensional spatial-spectral feature space into the principal feature representation space,which can effectively reduce band redundancy in the spectral dimension and decrease the computational complexity in the anomaly detection process.The feature enhancement method based on hyperbolic tangent function can significantly improve the distinction between anomalies and background noise to locate the abnormal target.

hyperspectral imageanomaly detectionmultichannel autoencodersweighted spatial-spectral Gaborhyperbolic tangent functionfeature enhancement method

贾森、刘宽、徐萌、朱家松

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深圳大学计算机与软件学院,深圳 518060

自然资源部大湾区地理环境监测重点实验室,深圳 518060

粤港澳智慧城市联合实验室,深圳 518060

高光谱图像 异常检测 多路自编码器 加权空谱Gabor 双曲正切函数 特征增强

国家自然科学基金国家自然科学基金国家自然科学基金广东省基础与应用基础研究基金广东省基础与应用基础研究基金深圳市科技计划深圳市科技计划深圳市科技计划Project of Department of Education of Guangdong Province

6227132741971300422713362022A15150112902021A1515011413RCJC20221008092731042JCYJ20220818100206015KQTD202009091139510052023KCXTD029

2024

遥感学报
中国地理学会环境遥感分会 中国科学院遥感应用研究所

遥感学报

CSTPCD北大核心
影响因子：2.921
ISSN：1007-4619
年,卷(期)：2024.28(1)
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