[2] Niu Y B, Wang B. Extracting target spectrum for hyperspectral target detection: an adaptive weighted learning method using a self-completed background dictionary[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 1604-1617(2017).
[3] Chen S X, Zhou Y F, Qi R L. Joint sparse representation of hyperspectral image classification based on kernel function[J]. Systems Engineering and Electronics, 40, 692-698(2018).
[5] Chang C I. A review of virtual dimensionality for hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 1285-1305(2018).
[6] AlSuwaidi A, Grieve B, Yin H J. Feature-ensemble-based novelty detection for analyzing plant hyperspectral datasets[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 1041-1055(2018).
[7] Tang Z Q, Fu G Y, Chen J et al. Low-rank structure based hyperspectral compression representation[J]. Journal of Electronics & Information Technology, 38, 1085-1091(2016).
[10] Sun B M, Guo Y, Li N et al. An efficient counting and localization framework for off-grid targets in WSNs[J]. IEEE Communications Letters, 21, 809-812(2017).
[11] Wang Y Y, Ren Y C, Chen L Y et al. Terahertz wave wide-beam imaging technology based on block compressive sensing theory[J]. Acta Optica Sinica, 39, 0411008(2019).
[12] Yu D P, Guo Y, Li N et al. Dictionary refinement method for compressive sensing based multi-target device-free localization[J]. Journal of Electronics & Information Technology, 41, 865-871(2019).
[13] Jiang Y, Wang B Q, Han J et al. Underdetermined wideband DOA estimation based on distributed compressive sensing[J]. Journal of Electronics & Information Technology, 41, 1690-1697(2019).
[14] Yin J H, Sun J Y, Jia X P. Sparse analysis based on generalized Gaussian model for spectrum recovery with compressed sensing theory[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2752-2759(2015).
[15] Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 53, 4655-4666(2007).
[17] Sun Y B, Wu Z B, Wu M et al. Compressed sensing reconstruction of hyperspectral imagery jointly using low rank and sparse prior[J]. Acta Electronica Sinica, 42, 2219-2224(2014).
[18] Fowler J E, Du Q. Reconstructions from compressive random projections of hyperspectral imagery[M]. ∥Optical Remote Sensing. Berlin, Heidelberg: Springer Berlin Heidelberg, 31-48(2011).
[19] Xu Y, Wu Z B, Chanussot J et al. Joint reconstruction and anomaly detection from compressive hyperspectral images using mahalanobis distance-regularized tensor RPCA[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 2919-2930(2018).
[20] Wang L, Feng Y. Compressed sensing reconstruction of hyperspectral images based on spatial-spectral multihypothesis prediction[J]. Journal of Electronics & Information Technology, 37, 3000-3008(2015).
[21] Wang Z L, Feng Y, Jia Y B. Spatio-spectral hybrid compressive sensing of hyperspectral imagery[J]. Remote Sensing Letters, 6, 199-208(2015).
[22] Wang L, Feng Y. Sparse decomposition of images based on particle swarm optimization[J]. Computer Simulation, 32, 363-367(2015).
[23] Wang L, Wang W, Chen B. Improved particle swarm optimization orthogonal matching pursuit reconstruction algorithm[J]. Journal of Chinese Computer Systems, 40, 1755-1759(2019).
[24] Cao J S. Space-time adaptive processing algorithms for airborne phase-array radar[D]. Chengdu: University of Electronic Science and Technology of China(2007).
[25] Li X L. A new intelligent optimization method-artificial fish school algorithm[D]. Hangzhou: Zhejiang University(2003).