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Hyperspectral anomaly detection based on spatial-spectral multichannel autoencoders

  • role： First author第一作者
  • Affiliation：

    Shenzhen University, College of Computer Science and Software Engineering, Shenzhen 518060, China

    Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources, Shenzhen 518060, China

    Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen 518060, China

  • Email：senjia@szu.edu.cn
  • Introduction：贾森，研究方向为人工智能和遥感信息处理。E-mail：senjia@szu.edu.cn
JIA Sen123,  
  • Affiliation：

    Shenzhen University, College of Computer Science and Software Engineering, Shenzhen 518060, China

LIU Kuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Shenzhen University, College of Computer Science and Software Engineering, Shenzhen 518060, China

    Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources, Shenzhen 518060, China

    Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen 518060, China

  • Email：m.xu@szu.edu.cn
  • Introduction：徐萌，研究方向为高光谱图像云检测和去除。E-mail：m.xu@szu.edu.cn
XU Meng123*,  
  • Affiliation：

    Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources, Shenzhen 518060, China

ZHU Jiasong2

реферат

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.

ключеви́че слова́

hyperspectral image;anomaly detection;multichannel autoencoders;weighted spatial-spectral Gabor;hyperbolic tangent function;feature enhancement method

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