Spatial-coordinated autoencoder for hyperspectral anomaly detection

Ganghui Fan; Yong Ma; Xiaoguang Mei; Jun Huang; Fan Fan; Hao Li

doi:10.11834/jig.210246

Remote Sensing Image Processing | Views : 0 下载量: 980 CSCD: 0

PDF
Export
Share
Collection
Album

Spatial-coordinated autoencoder for hyperspectral anomaly detection
Vol. 27, Issue 10, Pages: 3116-3126(2022)
Received：13 April 2021，

Revised：2021-6-10，

Accepted：17 June 2021，

Published：16 October 2022
DOI： 10.11834/jig.210246
稿件说明：

移动端阅览

DOI：

Ganghui Fan, Yong Ma, Xiaoguang Mei, Jun Huang, Fan Fan, Hao Li. Spatial-coordinated autoencoder for hyperspectral anomaly detection[J]. Journal of Image and Graphics, 2022, 27(10): 3116-3126. DOI： 10.11834/jig.210246.

摘要

目的

自编码器作为一种无监督的特征提取算法，可以在无标签的条件下学习到样本的高阶、稠密特征。然而当训练集含噪声或异常时，会迫使自编码器学习这些异常样本的特征，导致性能下降。同时，自编码器应用于高光谱图像处理时，往往会忽略掉空域信息，进一步限制了自编码器的探测性能。针对上述问题，本文提出一种基于空域协同自编码器的高光谱异常检测算法。

方法

利用块图模型优良的背景抑制能力从空域角度筛选用于自编码器训练的背景样本集。自编码器采用经预筛选的训练样本集进行网络参数更新，在提升对背景样本表达能力的同时避免异常样本对探测性能的影响。为进一步将空域信息融入探测结果，利用块图模型得到的异常响应构建权重，起到突出目标并抑制背景的作用。

结果

实验在3组不同尺寸的高光谱数据集上与5种代表性的高光谱异常检测算法进行比较。本文方法在3组数据集上的AUC(area under the curve)值分别为0.990 4、0.988 8和0.997 0，均高于其他算法。同时，对比了不同的训练集选择策略，与随机选取和使用全部样本进行对比。结果表明，本文基于空域响应的样本筛选方法相较对比方法具有较明显的优势。

结论

提出的基于空域协同自编码器的高光谱异常检测算法从空域角度筛选样本以提升自编码器区分异常与背景的能力，同时融合了光谱域和空域信息，进一步提升了异常检测性能。

Abstract

Objective

Hyperspectral imagery (HSI) consists of hundreds of narrow spectral bands and provides richer spectral information than infrared and multispectral images. Its features can distinguish targets from the background

and has been widely applied in many remote-sensing tasks

such as intelligent agriculture and mineral exploration. In many circumstance

however

targets are hard to be detected because of the existed prior spectral information is related to the background or targets. Anomaly detection can detect potential targets that differ from the surrounding background in an unsupervised manner. Anomalies focus on small scaled manual objects embedded in the surrounding background

and they differ from the background in terms of spectral information. As a typical unsupervised non-linear feature extractor

autoencoder (AE) contexts are applied in hyperspectral anomaly detection tasks. AE-based anomaly detection framework is getting more and more attention and many algorithms are developed to improve the detection performance. However

those AE-based anomaly detectors are hard to distinguish anomalies from the background due to lots of noise or outliers in training set. To minimize the objective function

the AE tends to learn the features of these noise and outliers. We facilitate spatial-coordinated autoencoder (ScAE) tackle these two issues mentioned above.

Method

Thanks to infrared patch-image model in suppressing the background and highlighting weak targets

a patch-image model is designed for hyperspectral images to be introduced in ScAE. Specifically

the patches are extended to form a matrix based on the first three components of HSI

the low-rank and sparse matrix decomposition (LRaSMD) is developed. The sparse part contains the target information and the background is suppressed. Thus

the training samples are opted based on the response of the decomposed sparse part

pixels with lower values and vice versa. The picked training samples are more clearly than utilizing the entire dataset or random selection strategy

and these samples are employed to update the weights of AE networks. The traditional gradients descend method and the backward propagation strategy is used to fine-tune AE. After some iterations

the vanilla AE is upgraded to be an effective hyperspectral anomaly detector. The reconstruction residual is utilized as the criterion to determine whether a pixel under the test (PUT) is anomalous or not. In order to take full advantage of the spatial information

a spatial-responses non-negative weight of patch-image model is introduced to increase the discrimination between the background and anomalies further

and the final detection result is obtained via fusing the spectral result and the weight matrix with Hadamard product.

Result

We evaluate our ScAE detector on three challenging hyperspectral image datasets with different size

namely Sandiego-1

Sandiego-2 and Botanical Garden. First

we design a set of experiment to figure out the issue of influential parameters for the final detection result. There are three parameters which are closely related with the detection performance. After extensive experiments

we found that ScAE is cohesive to the trade-off parameter and patch-related parameter

but is less to the number of hidden layers. Then

we conduct an ablation study to clarify the feasibility of the ScAE framework. The detection performance illustrates that patch-image model is optimal in suppressing the background while highlighting weak anomalies compared to attribute filter (AF). Our selection strategy gains higher area under the curve (AUC) values than randomly selection strategy. It is worth noting that a weight matrix related spatial and spectral information fusion can improve the detection result

as AUC values demonstrated. Finally

we compare our ScAE detector to three classical anomaly detectors and two popular hyperspectral anomaly detector

namely global Reed-Xiaoli (GRX)

LRaSMD-based Mahalanobis distance method (LSMAD)

traditional AE

fractional Fourier entropy (FrFE) and feature extraction and background purification anomaly detector (FEBPAD)

respectively. Such parameters are set to get optimal detection performance for the six detectors all. The corresponding AUC values of ScAE are 0.990 4

0.988 8 and 0.997 0 for Sandiego-1

Sandiego-2 and Botanical Garden

respectively. The detection result illustrates that ScAE obtains the highest AUC values amongst the six hypersepctral anomaly detectors. Moreover

ScAE generates the lowest false alarm rate (FAR) when all of the anomalous pixels are detected in three datasets all.

Conclusion

A novel AE-based hyperspectral anomaly detection algorithm is developed that spectral-detected spatial information can enhance the segmentation ability between anomalies and the background. The comparative experiments demonstrate our ScAE detector has its feasibility and potentials for hyperspectral anomaly detection.

关键词

Keywords

references

Cai J F, Candès E J and Shen Z W. 2010. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20(4): 1956-1982 [DOI: 10.1137/080738970]

Chang S Z, Du B and Zhang L P. 2019. A sparse autoencoder based hyperspectral anomaly detection algorithm using residual of reconstruction error//2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan: IEEE: 5488-5491 [ DOI:10.1109/IGARSS.2019.8898697 http://dx.doi.org/10.1109/IGARSS.2019.8898697 ]

Donoho D L. 1995. De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3): 613-627 [DOI: 10.1109/18.382009]

Gao C Q, Meng D Y, Yang Y, Wang Y T, Zhou X F and Hauptmann A G. 2013. Infrared patch-image model for small target detection in a single image. IEEE Transactions on Image Processing, 22(12): 4996-5009 [DOI: 10.1109/TIP.2013.2281420]

Imani M. 2017. RX anomaly detector with rectified background. IEEE Geoscience and Remote Sensing Letters, 14(8): 1313-1317 [DOI: 10.1109/LGRS.2017.2710618]

Kang X D, Zhang X P, Li S T, Li K L, Li J and Benediktsson J A. 2017. Hyperspectral anomaly detection with attribute and edge-preserving filters. IEEE Transactions on Geoscience and RemoteSensing, 55(10): 5600-5611 [DOI: 10.1109/TGRS.2017.2710145]

Li P H, Li M, Fan X N and Zhang X W. 2018. Hyperspectral anomaly detection algorithm based on iterative analysis with relative density. Journal of Image and Graphics, 23(2): 219-228

李普煌, 李敏, 范新南, 张学武. 2018. 迭代分析相对密度的高光谱异常检测. 中国图象图形学报, 23(2): 219-228 [DOI: 10.11834/jig.170243]

Liu K, Zhou Z, Li S Y, Liu Y F, Wan X, Liu Z W, Tan H and Zhang W F. 2020. Scene classification dataset using the Tiangong-1 hyperspectral remote sensing imagery and its applications. Journal of Remote Sensing, 24(9): 1077-1087

刘康, 周壮, 李盛阳, 刘云飞, 万雪, 刘志文, 谭洪, 张万峰. 2020. 天宫一号高光谱遥感场景分类数据集及应用. 遥感学报, 24(9): 1077-1087 [DOI: 10.11834/jrs.20209323]

Lu X Q, Zhang W X and Huang J. 2020. Exploiting embedding manifold of autoencoders for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 58(3): 1527-1537 [DOI: 10.1109/TGRS.2019.2944419]

Ma Y, Fan G H, Jin Q W, Huang J, Mei X G and Ma J Y. 2021. Hyperspectral anomaly detection via integration of feature extraction and background purification. IEEE Geoscience and Remote Sensing Letters, 18(8): 1436-1440 [DOI: 10.1109/LGRS.2020.2998809]

Ma Y, Jin Q W, Mei X G, Dai X B, Fan F, Li H and Huang J. 2019. Hyperspectral unmixing with Gaussian mixture model and low-rank representation. Remote Sensing, 11(8): #911 [DOI: 10.3390/rs11080911]

Mei X G, Pan E T, Ma Y, Dai X B, Huang J, Fan F, Du Q L, Zheng H and Ma J Y. 2019. Spectral-spatial attention networks for hyperspectral image classification. Remote Sensing, 11(8): #963 [DOI: 10.3390/rs11080963]

Reed I S and Yu X. 1990. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(10): 1760-1770 [DOI: 10.1109/29.60107]

Shi Y Z, Li J J, Yin Y P, Xi B B and Li Y S. 2019. Hyperspectral target detection with macro-micro feature extracted by 3-D residual autoencoder. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(12): 4907-4919 [DOI: 10.1109/JSTARS.2019.2939833]

Su Y C, Li J, Plaza A, Marinoni A, Gamba P and Chakravortty S. 2019. DAEN: deep autoencoder networks for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 57(7): 4309-4321[DOI: 10.1109/TGRS.2015.247929]

Tao R, Zhao X D, Li W, Li H C and Du Q. 2019. Hyperspectral anomaly detection by fractional Fourier entropy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(12): 4920-4929 [DOI: 10.1109/JSTARS.2019.2940278]

Wang H X, Dong H and Zhou Z Q. 2019. Review on dim small target detection technologies in infrared single frame images. Laser and Optoelectronics Progress, 56(8): #080001

王好贤, 董衡, 周志权. 2019. 红外单帧图像弱小目标检测技术综述. 激光与光电子学进展, 56(8): #080001 [DOI: 10.3788/LOP56.080001]

Xie W Y, Lei J, Liu B Z, Li Y S and Jia X P. 2019. Spectral constraint adversarial autoencoders approach to feature representation in hyperspectral anomaly detection. Neural Networks, 119: 222-234 [DOI: 10.1016/j.neunet.2019.08.012]

Xu Y, Wu Z B, Li J, Plaza A and Wei Z H. 2016. Anomaly detection in hyperspectral images based on low-rank and sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 54(4): 1990-2000 [DOI: 10.1109/TGRS.2015.2493201]

Zhang X P. 2017. Hyperspectral Image Anomaly Detection Method by Fusing Spatial and Spectral Information. Changsha: Hunan University: 19-20

张香萍. 2017. 联合空间与光谱信息的高光谱图像异常目标检测方法研究. 长沙: 湖南大学: 19-20

Zhang Y X, Du B, Zhang L P and Wang S G. 2016. A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 54(3): 1376-1389 [DOI: 10.1109/TGRS.2015.247929]

Zhu D H, Du B and Zhang L P. 2020. Band selection-based collaborative representation for anomaly detection in hyperspectral images. Journal of Remote Sensing, 24(4): 427-438

朱德辉, 杜博, 张良培. 2020. 波段选择协同表达高光谱异常探测算法. 遥感学报, 24(4): 427-438 [DOI: 10.11834/jrs.20209187]