Adaptive window based collaborative representation for hyperspectral anomaly detection with fusion of local and global information
Tài liệu tham khảo
Chen, 2022, Global to local: A hierarchical detection algorithm for hyperspectral image target detection, IEEE Trans. Geosci. Remote Sens., 60, 1
Choi, 2016, Subsampling-based acceleration of simple linear iterative clustering for superpixel segmentation, Comput. Vis. Image Underst., 146, 1, 10.1016/j.cviu.2016.02.018
Du, 2016, A spectral-spatial based local summation anomaly detection method for hyperspectral images, Signal Process., 124, 115, 10.1016/j.sigpro.2015.09.037
Elkholy, 2022, Unsupervised hyperspectral band selection with deep autoencoder unmixing, Int. J. Image Data Fusion, 13, 244, 10.1080/19479832.2021.1972047
Feng, 2022, Hyperspectral anomaly detection with total variation regularized low rank tensor decomposition and collaborative representation, IEEE GRSL, 19, 1
Gakhar, 2021, Spectral – spatial urban target detection for hyperspectral remote sensing data using artificial neural network, Egypt. J. Remote Sens. Space Sci., 24, 173
Guo, 2014, Weighted- RXD and linear filter-based RXD: Improving background statistics estimation for anomaly detection in hyperspectral imagery, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 7, 2351, 10.1109/JSTARS.2014.2302446
Hou, 2022, Collaborative representation with background purification and saliency weight for hyperspectral anomaly detection, Science China Inf. Sci., 65, 10.1007/s11432-020-2915-2
Hu, 2022, Hyperspectral anomaly detection using deep learning: A review, Remote Sens. (Basel), 14, 1973, 10.3390/rs14091973
Imani, 2017, RX anomaly detector with rectified background, IEEE GRSL, 14, 1313
Imani, 2018, Attribute profile based target detection using collaborative and sparse representation, Neurocomputing, 313, 364, 10.1016/j.neucom.2018.06.006
Imani, 2018, Anomaly detection using morphology-based collaborative representation in hyperspectral imagery, Eur. J. Remote Sens., 51, 457, 10.1080/22797254.2018.1446727
Imani, 2018, Manifold structure preservative for hyperspectral target detection, Adv. Space Res., 61, 2510, 10.1016/j.asr.2018.02.027
Imani, 2020, Sparse and collaborative representation-based anomaly detection, SIViP, 14, 1573, 10.1007/s11760-020-01709-0
Kang, 2020, Hyperspectral image visualization with edge-preserving filtering and principal component analysis, Information Fusion, 57, 130, 10.1016/j.inffus.2019.12.003
Küçük, S., Yüksel, S.E., 2015. Comparison of RX-based anomaly detectors on synthetic and real hyperspectral data. 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan, pp. 1-4.
Landgrebe, 1992, 220 Band Hyperspectral Image: AVIRIS Image Indian Pine Test Site 3, West Lafayette, Sch. Eng., Purdue Univ., Available Online
Li, 2015, Collaborative representation for hyperspectral anomaly detection, IEEE Trans. Geosci. Remote Sens., 53, 1463, 10.1109/TGRS.2014.2343955
Li, 2020, Hyperspectral anomaly detection with kernel isolation forest, IEEE TGRS, 58, 319
Li, 2023, Adaptively dictionary construction for hyperspectral target detection, IEEE Geosci. Remote Sens. Lett., 20, 5502005
Liu, 2022, Multipixel anomaly detection with unknown patterns for hyperspectral imagery, IEEE Trans. Neural Networks Learn. Syst., 33, 5557, 10.1109/TNNLS.2021.3071026
Liu, 2022, Joint optimization of autoencoder and self-supervised classifier: anomaly detection of strawberries using hyperspectral imaging, Comput. Electron. Agric., 198, 107007, 10.1016/j.compag.2022.107007
Lu, 2023, Coupled adversarial learning for fusion classification of hyperspectral and LiDAR data, Information Fusion, 93, 118, 10.1016/j.inffus.2022.12.020
Molero, 2013, Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 6, 801, 10.1109/JSTARS.2013.2238609
Paoletti, 2019, Deep learning classifiers for hyperspectral imaging: A review, ISPRS J. Photogramm. Remote Sens., 158, 279, 10.1016/j.isprsjprs.2019.09.006
Reed, 1990, Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution, IEEE Trans. Acoust. Speech Signal Process., 38, 1760, 10.1109/29.60107
Su, 2020, Low rank and collaborative representation for hyperspectral anomaly detection via robust dictionary construction, ISPRS J. Photogramm. Remote Sens., 169, 195, 10.1016/j.isprsjprs.2020.09.008
Taghipour, 2016, Anomaly detection of hyperspectral imagery using differential morphological profile, 1219
Tan, 2019, Anomaly detection for hyperspectral imagery based on the regularized subspace method and collaborative representation, Remote Sens. (Basel), 11, 1318, 10.3390/rs11111318
Wang, 2023, Hyperspectral anomaly detection using ensemble and robust collaborative representation, Inf. Sci., 624, 748, 10.1016/j.ins.2022.12.096
Wang, 2022, Hyperspectral anomaly detection via background purification and spatial difference enhancement, IEEE GRSL, 19, 1
Wang, 2022, Auto-AD: Autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder, IEEE TGRS, 60, 1
Wu, 2018, Hyperspectral anomalous change detection based on joint sparse representation, ISPRS J. Photogramm. Remote Sens., 146, 137, 10.1016/j.isprsjprs.2018.09.005
Wu, 2022, Hyperspectral anomaly detection with relaxed collaborative representation, IEEE TGRS, 60, 1
Xiang, 2022, Hyperspectral anomaly detection with guided autoencoder, IEEE TGRS, 60, 1
Xiang, 2022, Hyperspectral anomaly detection with local correlation fractional Fourier transform and vector pulse coupled neural network, Infrared Phys. Technol., 127, 10.1016/j.infrared.2022.104430
Xiao, 2023, Anomaly detection of hyperspectral images based on transformer with spatial-spectral dual-window mask, IEEE JSTARS., 16, 1414
Yang, 2022, Ensemble and random RX with multiple features anomaly detector for hyperspectral image, IEEE GRSL, 19, 1
Zhang, G., Xu, M., Zhang, Y., Fan, Y., 2019. Improved Hyperspectral Anomaly Target Detection Method Based On Mean Value Adjustment, 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, pp. 1-4, Amsterdam, Netherlands.
Zhao, 2023, A joint method of spatial–spectral features and BP neural network for hyperspectral image classification, Egypt. J. Remote Sens. Space Sci., 26, 107