Filter tensor analysis: A tool for multi-temporal remote sensing target detection

Albanwan, 2018, A novel spectrum enhancement technique for multi-temporal, multi-spectral data using spatial-temporal filtering, ISPRS J. Photogramm. Rem. Sens., 142, 51, 10.1016/j.isprsjprs.2018.05.020 Axelsson, 2016, Target detection in hyperspectral imagery using forward modeling and in-scene information, ISPRS J. Photogramm. Rem. Sens., 119, 124, 10.1016/j.isprsjprs.2016.05.008 Chang, 2000, Generalized constrained energy minimization approach to subpixel detection for multispectral imagery, Opt. Eng., 39, 1275, 10.1117/1.602486 Dronova, 2011, Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at poyang lake, china, Rem. Sens. Environ., 115, 3220, 10.1016/j.rse.2011.07.006 Du, 2003, A comparative study for orthogonal subspace projection and constrained energy minimization, IEEE Trans. Geosci. Rem. Sens., 41, 1525, 10.1109/TGRS.2003.813704 Farrand, 1997, Mapping the distribution of mine tailings in the coeur d’alene river valley, idaho, through the use of a constrained energy minimization technique, Rem. Sens. Environ., 59, 64, 10.1016/S0034-4257(96)00080-6 Geng, 2016, Clever eye algorithm for target detection of remote sensing imagery, ISPRS J. Photogramm. Rem. Sens., 114, 32, 10.1016/j.isprsjprs.2015.10.014 Geng, 2014, Cem: More bands, better performance, IEEE Geosci. Rem. Sens. Lett., 11, 1876, 10.1109/LGRS.2014.2312319 Geng, 2018, A piecewise linear strategy of target detection for multispectral/hyperspectral image, IEEE J. Selec. Top. Appl. Earth Observ. Rem. Sens., 1 Geng, 2017, The match filter (mf) is always superior to constrained energy minimization (cem), Rem. Sens. Lett., 8, 696, 10.1080/2150704X.2017.1312616 Gong, 2013, Finer resolution observation and monitoring of global land cover: first mapping results with landsat tm and etm+ data, Int. J. Rem. Sens., 34, 2607, 10.1080/01431161.2012.748992 Fu, Y.H., Piao, S., Zhao, H., Jeong, S.J., Wang, X., Vitasse, Y., Ciais, P., Janssens, I.A. Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes. Glob. Change Biol., 20, 3743–3755. URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.12610, https://doi.org/10.1111/gcb.12610, arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.12610. Harsanyi, 1993 Harsanyi, 1994, Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach, IEEE Trans. Geosci. Rem. Sens., 32, 779, 10.1109/36.298007 Ji, 2015, Target detection method for water mapping using landsat 8 oli/tirs imagery, Water, 7, 794, 10.3390/w7020794 Ji, 2015, Improving the accuracy of the water surface cover type in the 30 m from-glc product, Rem. Sens., 7, 13507, 10.3390/rs71013507 Ji, L., Gong, P., Wang, J., Shi, J., Zhu, Z., 2018. Construction of the 500-m resolution daily global surface water change database (2001–2016). Water Resour. Res., 54, 10, 270–10,292. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR023060, https://doi.org/10.1029/2018WR023060, arXiv:https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR023060. Keenan, 2014, Net carbon uptake has increased through warming-induced changes in temperate forest phenology, Nat. Clim. Change, 4, 598, 10.1038/nclimate2253 Kwon, 2005, Kernel orthogonal subspace projection for hyperspectral signal classification, IEEE Trans. Geosci. Rem. Sens., 43, 2952, 10.1109/TGRS.2005.857904 Kwon, 2007, Kernel spectral matched filter for hyperspectral imagery, Int. J. Comput. Vision, 71, 127, 10.1007/s11263-006-6689-3 Lin, 2018, Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques, ISPRS J. Photogramm. Rem. Sens., 142, 174, 10.1016/j.isprsjprs.2018.05.022 Lu, 2016, Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from modis time series, ISPRS J. Photogramm. Rem. Sens., 117, 227, 10.1016/j.isprsjprs.2016.03.007 Makki, 2017, A survey of landmine detection using hyperspectral imaging, ISPRS J. Photogramm. Rem. Sens., 124, 40, 10.1016/j.isprsjprs.2016.12.009 Manolakis, 2003, Hyperspectral image processing for automatic target detection applications, Lincoln Laborat. J., 14, 79 Manolakis, 2002, Detection algorithms for hyperspectral imaging applications, IEEE Signal Process. Mag., 19, 29, 10.1109/79.974724 Manolakis, 2001, Adaptive matched subspace detectors for hyperspectral imaging applications, vol. 5, 3153 Manolakis, 2001, Hyperspectral subpixel target detection using the linear mixing model, IEEE Trans. Geosci. Rem. Sens., 39, 1392, 10.1109/36.934072 McCann, 2017, Multi-temporal mesoscale hyperspectral data of mixed agricultural and grassland regions for anomaly detection, ISPRS J. Photogramm. Rem. Sens., 131, 121, 10.1016/j.isprsjprs.2017.07.015 Shen, M., Piao, S., Cong, N., Zhang, G., Jassens J.I. Precipitation impacts on vegetation spring phenology on the tibetan plateau. Global Change Biology 21, 3647–3656. URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.12961, https://doi.org/10.1111/gcb.12961, arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.12961. Spivak, 1965 Wang, 2015, Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250m resolution, ISPRS J. Photogramm. Rem. Sens., 103, 38, 10.1016/j.isprsjprs.2014.03.007 Wang, 2013, A kernel-based target-constrained interference-minimized filter for hyperspectral sub-pixel target detection, IEEE J. Selec. Top. Appl. Earth Observ. Rem. Sens., 6, 626, 10.1109/JSTARS.2013.2251863 Wehmann, 2015, A spatialtemporal contextual markovian kernel method for multi-temporal land cover mapping, ISPRS J. Photogramm. Rem. Sens., 107, 77, 10.1016/j.isprsjprs.2015.04.009 Youden, 1950, Index for rating diagnostic tests, Cancer, 3, 32, 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3 Zhang, 2017