Hyperspectral Anomaly Detection Based on 3D Convolutional Autoencoder and Low Rank Representation

Bangyong SUN; Zhe ZHAO; Bingliang HU; Tao YU

doi:10.3788/gzxb20215004.0410003

[1] Xing ZHANG, Gongjian WEN, Wei DAI. A tensor decomposition-based anomaly detection algorithm for hyperspectral image. IEEE Transactions on Geoscience and Remote Sensing, 54, 5801-5820(2016).

[2] S Reed, Xiaoli YU. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. Signal Processing, 38, 1760-1770(1990).

[3] J MOLERO, E GARZON, I GARCIA et al. Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 801-814(2013).

[4] Qiandong GUO, Bing ZHANG, Qiong RAN et al. Weighted-RXD and linear filter-based RXD： Improving background statistics estimation for anomaly detection in hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 2351-2366(2014).

[5] H KWON, N NASRABADI. Kernel RX-algorithm： a nonlinear anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 43, 388-397(2015).

[6] Wei LI, Qian DU. Collaborative representation for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 53, 1463-1474(2015).

[7] Ning MA, Yu PENG, Shaojun WANG. A fast recursive collaboration representation anomaly detector for hyperspectral image. IEEE Geoscience and Remote Sensing Letters, 16, 588-592(2018).

[8] Hongjun SU, Zhaoyue WU, Qian DU et al. Hyperspectral anomaly detection using collaborative representation with outlier removal. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 1-10(2018).

[9] Jiayi LI, Hongyan ZHANG, Liangpei ZHANG et al. Hyperspectral anomaly detection by the use of background joint sparse representation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2523-2533(2015).

[10] Shuangjiang LI, Wei WANG, Hairong QI et al. Low-rank tensor decomposition based anomaly detection for hyperspectral imagery, 4525-4529(2015).

[11] Wei WANG, Shuangjiang LI, Hairong QI et al. Identify anomaly component by sparsity and low rank, 7, 1-4(2015).

[12] Yuxiang ZHANG, Bo DU, Liangpei ZHANG et al. A low-rank and sparse matrix decomposition-based Mahalanobis distance method for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 54, 1376-1389(2015).

[13] Shangzhen SONG, Huixin ZHOU, Yixin YANG et al. Hyperspectral anomaly detection via convolutional neural network and low rank with density-based clustering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 3637-3649(2019).

[14] Ying QU, Wei WANG, Rui GUO et al. Hyperspectral anomaly detection through spectral unmixing and dictionary-based low-rank decomposition. IEEE Transactions on Geoscience and Remote Sensing, 56, 4391-4405(2018).

[15] Qian DU, Wei LI, Guodong WU. Transferred deep learning for anomaly detection in hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 14, 597-601(2017).

[16] Ning MA, Yu PENG, Shaojun WANG et al. An unsupervised deep hyperspectral anomaly detector. Sensors, 18, 693(2018).

[17] Chunhui ZHAO, Xueyuan LI, Haifeng ZHU. Hyperspectral anomaly detection based on stacked denoising autoencoders. Journal of Applied Remote Sensing, 11, 042605(2017).

[18] Chunhui ZHAO, Lili ZHANG. Spectral-spatial stacked autoencoders based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection. Infrared Physics & Technology, 92, 166-176(2018).

[19] Jie LEI, Weiying XIE, Jian YANG et al. Spectral-spatial feature extraction for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 57, 8131-8143(2019).

[20] Shuiwang JI, Wei XU, Ming YANG et al. 3D convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence, 35, 221-231(2012).

[21] Shaohui MEI, Jingyu JI, Yunhao GENG et al. Unsupervised spatial-spectral feature learning by 3d convolutional autoencoder for hyperspectral classification. IEEE Transactions on Geoscience and Remote Sensing, 57, 6808-6820(2019).

[22] Yanzi SHI, Jiaojiao LI, Yaping YIN et al. 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, 4907-4919(2019).

[23] M ESTER, H KRIEGEI, J SANDER et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, 9634, 226-231(1996).

[24] Xiaohu ZHANG, Yuexian ZOU, Wei SHI. Dilated convolution neural network with LeakyReLU for environmental sound classification, 1-5.

[25] Qingsong YANG, Pingkun YAN, Yanbo ZHANG et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE transactions on medical imaging, 37, 1348-1357(2018).

[26] F KRUSE, A LEFKOFF, W BOARDMAN et al. The spectral image processing system （SIPS）-interactive visualization and analysis of imaging spectrometer data, 283, 192-201(1993).

[27] Ailin LI, Zhaowei SHANG. A new spectral-spatial pseudo-3d dense network for hyperspectral image classification, 1-7(2019).

[28] Yang XU, Zebin WU, Jun LI et al. Anomaly detection in hyperspectral images based on low-rank and sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 54, 1990-2000(2016).

[29] E CANDES, Xiaodong LI, Yi MA et al. Robust principal component analysis？. Journal of the ACM （JACM）, 58, 1-37(2011).

[30] Weiwei SUN, Chun LIU, Jialin LI et al. Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery. Journal of Applied Remote Sensing, 8, 083641(2014).

[31] A TAGHIPOUR, H GHASSEMIAN. Unsupervised hyperspectral target detection using spectral residual of deep autoencoder networks, 52-57(2019).

[32] Yong MA, Ganghui FAN, Qiwen JIN et al. Hyperspectral anomaly detection via integration of feature extraction and background purification. IEEE Geoscience and Remote Sensing Letters.