|
|
|
|
|
|
| Hyperspectral Anomaly Detection Based on 3D Convolutional
Autoencoder Network |
| WANG Sheng-ming1, WANG Tao1*, TANG Sheng-jin2, SU Yan-zhao1 |
1. Combat Support Academy，Rocket Force University of Engineering, Xi’an 710025, China
2. Missile Engineering Academy, Rocket Force University of Engineering, Xi’an 710025, China
|
|
|
|
|
Abstract Hyperspectral images contain abundant spectral information of ground objects and have great development prospects in the field of remote sensing images. Anomaly detection of hyperspectral images can detect abnormal targets in images without any prior spectral information. Therefore, it is widely used in national, military, and civil fields, and it is a research hotspot in hyperspectral image processing at present. However, hyperspectral images are characterized by complex data, strong redundancy, unlabeled and small number of samples, which brings great challenges to anomaly detection of hyperspectral images. Especially in deep learning, large image data is often needed as training samples, which is difficult to obtain hyperspectral images. Aiming at the problems that most existing algorithms are not adaptive to hyperspectral images and lack of space-spectral information utilization, a hyperspectral anomaly detection algorithm based on 3D convolution autoencoder network is proposed, which can effectively utilize hyperspectral image information, learn more discriminative feature expression, and improve detection accuracy under the premise of a small amount of training data. Firstly, the 3D convolution network is designed through 3D convolution, 3D pooling and 3D normalization, and then the spatial-spectral structure features of hyperspectral images are extracted. Then, the 3D convolution network and the 3D deconvolution network are embedded into the auto and decoder of the autoencoder network, respectively. background reconstruction is carried out by minimizing the reconstruction error combining the mean square error and the spectral angular distance. Finally, the Mahalanobis distance between the original hyperspectral image and the reconstructed background image is used for anomaly detection. This algorithm can automatically train all parameters in the network without prior information, learn the effective features of hyperspectral images and carry out background reconstruction in an unsupervised way. It is performed using the nine images from three sets of real high spectral data sets and is compared with the five algorithms of RX, SRX, CRD, UNRS, and LRASR. The experimental results show that this algorithm maintains a high detection effect and accuracy in the context of high spectrum images compared to existing algorithms.
|
|
Received: 2021-03-13
Accepted: 2021-05-17
|
|
|
|
Corresponding Authors:
WANG Tao
E-mail: 941947114@qq.com
|
|
[1] Gomb P, Romaszewski M. In: Hyperspectral Remote Sensing: Theory and Applications, Elsevier, 2020. 45.
[2] Patro R N, Subudhi S, Biswal P K, et al. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(3): 72.
[3] Xu Y, Wu Z B, Li J, et al. IEEE Transactions on Geoscience & Remote Sensing, 2016, 54(4): 1990.
[4] Zhang Y, Du B, Zhang L, et al. IEEE Transactions on Geoscience & Remote Sensing, 2016, 54(3): 1376.
[5] Zhang X, Huang W, Wang Q, et al. IEEE Transactions on Geoscience & Remote Sensing, 2021, 59(7): 5953.
[6] Bati E, Çalişkan A, Koz A, et al. Proc SPIE，2015, 9643: 96430N.
[7] Li W, Wu G, Du Q. IEEE Geoscience and Remote Sensing Letters, 2017, 14(5): 597.
[8] Zhao C, Li X, Zhu H. Journal of Applied Remote Sensing, 2017, 11(4): 042605.
[9] Jiang T, Li Y, Xie W, et al. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4666.
[10] Wu Z, Shi L, Li J, et al. IEEE Journal of Selected Topics in Applied Eeath Observations and Remote Sensing, 2018, 11(4): 1131.
[11] Mei S, Ji J, Geng Y, et al. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6808.
[12] Liu L, Sun M, Ren X, et al. Journal of Visual Communication and Image Representation, 2020, 71: 102721.
[13] Xue B, Yu C, Wang Y, et al. IEEE Transactions on Geosciense and Remote Sensing, 2017, 55(9): 5093
|
| [1] |
WANG Cai-ling1，ZHANG Jing1，WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
| [2] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [3] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
| [4] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
| [5] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [6] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [7] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
| [8] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
| [9] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
| [10] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
| [11] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
| [12] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
| [13] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
| [14] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
| [15] |
TAO Jing-zhe1, 3, SONG De-rui1, 3, SONG Chuan-ming2, WANG Xiang-hai1, 2*. Multi-Band Remote Sensing Image Sharpening: A Survey[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2999-3008. |
|
|
|
|
