Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior

المفاهيم الأساسية

The proposed Self-Supervised Anomaly Prior (SAP) method utilizes a self-supervised network to learn the characteristics of hyperspectral anomalies, which is then plugged into a low-rank representation model to provide a more accurate and interpretable solution for hyperspectral anomaly detection.

الملخص

The paper proposes a novel low-rank representation (LRR) model with Self-Supervised Anomaly Prior (SAP) for hyperspectral anomaly detection (HAD). The key innovations are:

SAP is the first study to obtain the anomaly prior by deep learning in the HAD field, which comprehensively considers the spatial and spectral characteristics of anomalies, and is independent of manually set parameters.

To obtain a prior that can generalize various anomalies in the lack of labels and limited samples, a customized pretext task is designed as distinguishing the original hyperspectral image (HSI) and the pseudo-anomaly HSI generated from the original HSI.

To obtain an enriched background dictionary without anomaly contamination, a dual-purified strategy for dictionary construction is proposed.

The proposed method first utilizes self-supervised learning to train a network to capture the characteristics of hyperspectral anomalies. The well-trained network is then plugged into the LRR model as the anomaly prior to solve the anomaly sub-problem. Additionally, a dual-purified strategy is used to construct the background dictionary, providing a more refined background representation. Extensive experiments on real hyperspectral datasets demonstrate that the proposed SAP offers a more accurate and interpretable solution than other advanced HAD methods.

الإحصائيات

The occurrence probability of anomalous pixels is relatively low, and their correlation is not as strong as that among background pixels.
The proposed method divides the input HSI into small cubes and computes the anomaly score of each cube using the Mahalanobis distance.
The initial detection map is obtained by propagating the anomaly scores through Gaussian smoothing, and then an adaptive threshold segmentation is performed to generate the final detection map.

اقتباسات

"The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anomaly component is optimized by handcrafted sparse priors (e.g., ℓ2,1-norm). However, this may not be ideal since they overlook the spatial structure present in anomalies and make the detection result largely dependent on manually set sparsity."
"To tackle these problems, we redefine the optimization criterion for the anomaly component in the LRR model with a self-supervised network called self-supervised anomaly prior (SAP). This prior is obtained by the pretext task of self-supervised learning, which is customized to learn the characteristics of hyperspectral anomalies."

الرؤى الأساسية المستخلصة من

Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior

by Yidan Liu,We... في arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13342.pdf

استفسارات أعمق

How can the proposed SAP method be extended to handle hyperspectral images with different spatial and spectral resolutions

The proposed SAP method can be extended to handle hyperspectral images with different spatial and spectral resolutions by incorporating adaptive strategies for anomaly detection. One approach could involve developing a multi-resolution anomaly detection framework that can adapt to varying spatial and spectral resolutions. This could involve pre-processing steps to normalize the spatial and spectral dimensions of the input hyperspectral images before applying the anomaly detection algorithm. Additionally, the anomaly detection model could be designed to dynamically adjust its parameters based on the resolution of the input data. By incorporating adaptive mechanisms, the SAP method can effectively handle hyperspectral images with different spatial and spectral resolutions, ensuring robust anomaly detection performance across diverse datasets.

What are the potential limitations of the pseudo-anomaly generation approach, and how can it be further improved to better capture the diversity of real-world hyperspectral anomalies

The pseudo-anomaly generation approach, while effective in capturing the sparsity and spatial characteristics of hyperspectral anomalies, may have limitations in fully representing the diversity of real-world anomalies. One potential limitation is the artificial nature of the generated pseudo-anomalies, which may not fully mimic the complexity and variability of actual anomalies present in hyperspectral images. To address this limitation, the approach can be further improved by incorporating more sophisticated anomaly generation techniques, such as generative adversarial networks (GANs) or data augmentation methods that introduce more realistic anomalies into the training data. Additionally, incorporating domain-specific knowledge and expert input can help in designing more diverse and representative pseudo-anomalies that better reflect the anomalies present in real-world hyperspectral images. By enhancing the diversity and realism of the generated anomalies, the pseudo-anomaly generation approach can be improved to provide more accurate and comprehensive anomaly detection capabilities.

Given the success of SAP in hyperspectral anomaly detection, how can the self-supervised learning framework be applied to other remote sensing tasks, such as land cover classification or change detection

Given the success of SAP in hyperspectral anomaly detection, the self-supervised learning framework can be applied to other remote sensing tasks, such as land cover classification or change detection, by adapting the pretext tasks and target tasks to suit the specific objectives of these tasks. For land cover classification, the pretext task could involve distinguishing between different land cover types in the input images, while the target task could focus on refining the classification results based on the learned features. Similarly, for change detection, the pretext task could involve identifying changes between two consecutive images, while the target task could aim to accurately detect and localize the changes. By customizing the self-supervised learning framework to the requirements of land cover classification and change detection tasks, the approach can leverage the benefits of unsupervised learning to improve the accuracy and efficiency of these remote sensing applications.