Recursive RX with Extended Multi-Attribute Profiles for Hyperspectral Anomaly Detection
Abstract
:1. Introduction
- 1
- The extended multi-attribute profile (EMAP) is used to extract spatial features of hyperspectral images, which can improve the detection performance by combining the spatial information.
- 2
- According to the goal of anomaly detection, the RX detector is adopted to remove the pixels that are more likely to be anomalies, which can purify the background and help estimate the background distribution.
- 3
- The RX model is used on the purified image to detecte the anomaly.
2. Related Work
2.1. Global RX
2.2. Local RX
3. Proposed Method
3.1. Extended Multi-Attribute Profiles
Algorithm 1: EMAP |
Input: Hyperspectral data , the number of principal components c.
|
3.2. Recursive RX Using Extended Multi-Attribute Profiles
Algorithm 2: RRXEMAP algorithm |
Input: Hyperspectral data , the number of principal components c.
|
4. Experiments
- 1
- AVIRIS-I Dataset: This dataset was acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) in San Diego, CA, USA. The whole image scene has pixels, with 224 spectral bands in wavelengths ranging from 370 to 2510 nm. The AVIRIS-I dataset is a region with a size of pixels from the top left of this image, which was obtained from [38]. After removing the water absorption, low-signal-to-noise ratio, and poor-quality bands, only 189 bands remained in the experiments. In this scene, three airplanes composed of 58 pixels are regarded as anomalies, which need to be detected. The false color image and the corresponding ground truth map of the AVIRIS-I dataset are shown in Figure 3a and Figure 3d, respectively.
- 2
- AVIRIS-II Dataset: Compared with the AVIRIS-I dataset, the AVIRIS-II dataset is a region with a size of pixels from the center of San Diego, which was obtained from [70]. In this image, three airplanes composed of 134 pixels are regarded as anomalies, which need to be detected. The false color image and the corresponding ground truth map of the AVIRIS-I dataset are presented in Figure 3b and Figure 3e, respectively.
- 3
- Cri Dataset: This dataset was obtained by the Nuance Cri hyperspectral sensor and acquired from [38], with a size of pixels and 46 spectral bands in wavelengths ranging from 650 to 1100 nm. There are ten rocks represented by 2216 pixels regarded as anomalies to be detected in this image scene. The false color image and the corresponding ground truth map of the AVIRIS-I dataset are presented in Figure 3c and Figure 3f, respectively.
- 4
- abu-airport-2 Dataset: This dataset was acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) from ABU datasets [70], with a size of pixels and 204 spectral bands. The two airports are regarded as anomalies in this image scene. The false color image and the corresponding ground truth map of the abu-airport-2 dataset are presented in Figure 4a and Figure 4d, respectively.
- 5
- abu-urban-1 Dataset: This dataset is an urban scene image from ABU datasets [70], with a size of pixels and 204 spectral bands. Various vehicles and man-made targets are regarded as anomalies in this image scene. The false color image and the corresponding ground truth map of the abu-urban-1 dataset are presented in Figure 4b and Figure 4e, respectively.
- 6
- 7
- Salinas-simulate Dataset: This dataset has a synthetic anomaly, which was obtained by using the target implant method on a different portion of the Salinas scene. The Salinas scene was collected over Salinas Valley, California [71]. The whole scene has pixels, 16 classes, with 224 spectral bands. After removing the water absorption, low-signal-to-noise ratio, and poor-quality bands, only 204 bands are valuable. The false color image consists of bands 70, 27 and 17 and the corresponding ground truth map is respectively provided in Figure 5a,b.The Salinas-simulate dataset covers a region with a size of pixels and 204 bands from the Salinas scene, which was obtained from [71]. In this scene, a binary mask image shown in Figure 5d has been constructed by generating six squares, which have sides measuring from 1 to 6 pixels arranged in a line. The six squares have been copied in reverse order and arranged in another line at close distance. The two lines have been rotated by an angle of approximatively . The value of pixels inside the squares is 1, while that of the rest of the pixels in is 0. Next, a region is cropped from the Salinas scene with the same dimension as the mask. The modified image can be built, which contains the implanted target as follows:
4.1. Evaluation Methods
4.2. Compared Methods
- 1
- GRX: Global RX (GRX) is the representative hyperspectral imagery anomaly detector, which models the background using the whole image.
- 2
- LRX: Local RX (LRX) is the version of RX, which models the background using the local dual-window image. The detection performance of the LRX is sensitive to the sliding double window sizes. In this paper, the inner window size ranges from 5 to 11 and the outer window size ranges from 7 to 13 to be selected optimally.
- 3
- CRD: The collaborative representation-based anomaly detection (CRD) algorithm for the HSIs assumes that each background pixel can be approximately represented by its surrounding pixels in a local dual window. CRD is also sensitive to the sliding double window sizes; the inner window size (ranging from 5 to 11) and the outer window size (ranging from 7 to 13) are selected optimally.
- 4
- LRaSMD: The low-rank and sparse matrix decomposition (LRaSMD) method scores each pixel according to the Euclidean distance between the corresponding sparse component vector and the mean vector of the sparse matrix.
- 5
- LSMAD: The low-rank and sparse matrix decomposition-based Mahalanobis distance (LSMAD) is a typical low-rank and sparse matrix decomposition-based detector, which explores the low-rank prior knowledge of the background pixels and scores each pixel according to the Mahalanobis distance. The parameter of LSMAD refers to the original setting as [62].
4.3. Detection Result
4.4. Background Purity
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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He, F.; Yan, S.; Ding, Y.; Sun, Z.; Zhao, J.; Hu, H.; Zhu, Y. Recursive RX with Extended Multi-Attribute Profiles for Hyperspectral Anomaly Detection. Remote Sens. 2023, 15, 589. https://doi.org/10.3390/rs15030589
He F, Yan S, Ding Y, Sun Z, Zhao J, Hu H, Zhu Y. Recursive RX with Extended Multi-Attribute Profiles for Hyperspectral Anomaly Detection. Remote Sensing. 2023; 15(3):589. https://doi.org/10.3390/rs15030589
Chicago/Turabian StyleHe, Fang, Shuai Yan, Yao Ding, Zhensheng Sun, Jianwei Zhao, Haojie Hu, and Yujie Zhu. 2023. "Recursive RX with Extended Multi-Attribute Profiles for Hyperspectral Anomaly Detection" Remote Sensing 15, no. 3: 589. https://doi.org/10.3390/rs15030589