Objective

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The continuous improvement in spectral resolution promotes the development and progress of hyperspectral remote sensing technology. Hyperspectral remote sensing technology has broad application scenarios and great application value and is a major research topic in the field of remote sensing. Anomaly detection of the hyperspectral image is an important branch in the field of hyperspectral remote sensing

and it is widely used in the industry

geological exploration

and other fields. The number of anomalies in the scene is usually small

and the spatial and spectral characteristics are different from the surrounding background. In the hyperspectral field

the spectral characteristics of pixels are usually used to distinguish the background and anomalous targets

that is

the anomalous pixels in the image are searched through the spectral differences. The detection methods based on a statistical model and traditional machine learning will have difficulty building the background model because of the complexity of background pixels. They will also have difficulty selecting the form of kernel function and building the background dictionary due to the lack of prior knowledge. Moreover

traditional anomaly detection algorithms do not effectively mine the deep features of the spectrum

and the rich spectral information in hyperspectral images is not fully utilized. Deep learning has great advantages in processing complex hyperspectral images

and anomaly detection method using deep learning is still a frontier area worthy of exploration and has gradually become the focus of research. Therefore

this study proposes a hyperspectral image anomaly detection method based on siamese neural network with pixel-pair strategy. It uses deep learning technology to extract the deep nonlinear features of hyperspectral images. This method aims to improve the accuracy of anomaly detection and promote the development of hyperspectral image processing and application technology.

Method

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The method of anomaly detection in the hyperspectral image based on siamese neural network with pixel-pair feature (PPF-SNN) is divided into three steps. First

the idea of pixel-pair is adopted to amplify training samples because of the scarcity of hyperspectral data samples with real labels and the need for a large number of training data of the deep network model. Specifically

two pixels are randomly selected from the reference data containing multiple types of ground materials for matching. If they come from the same labeled class

then the pair is labeled as 0; if they come from different labeled classes

then the label is 1. Compared with the original datasets

the number of new datasets obtained by pairing increases exponentially to meet the demand of a deep network for the number of datasets. Second

we build a siamese network model with a feature extraction module and feature processing module. The branch network of the feature extraction module adopts the convolutional neural network (CNN) structure with weight sharing

which contains 10 convolutional layers. The feature processing module concatenates the input feature pairs and then extracts the difference features of pixel pairs through a convolutional layer

while the pixel pairs are classified through the fully connected layers. The module uses the extracted pixel pair features to measure the similarity of the input pairs. Then

the new training dataset is used to train the model

and the trained classification model is transferred to the detection process with fixed parameters. Third

the sliding dual-window strategy is used to pair the test set

and the test pixel pair dataset is sent to the network model. Next

the difference score of each pixel compared with the surrounding background pixels is obtained. If the score is close to 1

then the pixel under test tends to be anomalous. If the score is close to 0

then the pixel under test is close to the background. Using this principle

we can identify the anomalous targets in the test scene.

Result

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To verify the effectiveness of the proposed algorithm

the experimental part selects two scenes from the San Diego dataset and one scene from the ABU-Airport dataset and uses the traditional algorithms like global RXD (GRXD)

local RXD (LRXD)

and collaborative representation-based detector (CRD) and the anomaly detection algorithm based on convolutional neural network with pixel-pair feature (PPF-CNN) as the comparative algorithms. The receiver operating curve (ROC) of each algorithm is drawn

and the corresponding area under the ROC curve (AUC) value is calculated as an evaluation index of algorithm performance. In the anomaly detection experimental results of the three scenes

the proposed PPF-SNN has the highest AUC values of 0.993 51

0.981 21

and 0.984 38

respectively. It can ensure the highest detection rate while keeping the false alarm rate low. The performance of PPF-SNN has obvious advantages over traditional algorithms and PPF-CNN algorithm.

Conclusion

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The proposed hyperspectral image anomaly detection method based on siamese neural network can extract the deep spectral characteristics of the input pixel pair. According to the difference in its characteristics

the network will learn the distinction between the two. Thus

it can effectively provide the anomaly score of the pixel under test relative to the surrounding background. Compared with PPF-CNN

the proposed method can effectively reduce false alarms. It can also highlight anomalous targets more obviously

improve the detection rate

and exhibit stronger robustness than traditional methods.