代先强, 杨盛刚, 肖鹏, 等. 渝东北烟区土壤退化现状剖析[J]. 西南大学学报(自然科学版), 2023, 45(12): 65-75.
王智, 杨胜刚, 范业晨, 等. 重庆市石柱县烟田土壤养分空间异质性分布及评价[J]. 西南大学学报(自然科学版), 2023, 45(11): 42-52.
杨鉴, 张珍明, 陈祖拥, 等. 贵州省典型茶园土壤锌含量空间异质性及影响因素[J]. 东北农业大学学报, 2023, 54(12): 21-31.
杨梅, 胡晓婷, 徐卫红. 不同类型土壤与辣椒风味品质的相关性研究[J]. 西南大学学报(自然科学版), 2024, 46(1): 2-16.
李恬, 李怀刚, 何建军, 等. 陆面资料对复杂地形气温和降水模拟的影响——以济南市为例[J]. 西南大学学报(自然科学版), 2023, 45(9): 124-131.
莫金宵, 雷冬梅, 李杰, 等. 县级自然保护区土地利用景观格局与固碳功能关系分析——以云南省梁王山为例[J]. 云南农业大学学报(自然科学), 2023, 38(4): 694-703.
HEYLEN R, PARENTE M, GADER P. A Review of Nonlinear Hyperspectral Unmixing Methods[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 1844-1868. doi: 10.1109/JSTARS.2014.2320576
LI S T, SONG W W, FANG L Y, et al. Deep Learning for Hyperspectral Image Classification: An Overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6690-6709. doi: 10.1109/TGRS.2019.2907932
PALSSON B, SIGURDSSON J, SVEINSSON J R, et al. Hyperspectral Unmixing Using a Neural Network Autoencoder[J]. IEEE Access, 2018, 6: 25646-25656. doi: 10.1109/ACCESS.2018.2818280
SHI S K, ZHAO M, ZHANG L J, et al. Probabilistic Generative Model for Hyperspectral Unmixing Accounting for Endmember Variability[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5516915.
PALSSON B, ULFARSSON M O, SVEINSSON J R. Convolutional Autoencoder for Spectral-Spatial Hyperspectral Unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1): 535-549. doi: 10.1109/TGRS.2020.2992743
YU Y, MA Y, MEI X G, et al. Multi-Stage Convolutional Autoencoder Network for Hyperspectral Unmixing[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 113: 102981. doi: 10.1016/j.jag.2022.102981
GHOSH P, ROY S K, KOIRALA B, et al. Hyperspectral Unmixing Using Transformer Network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5535116.
DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An Image Is Worth 16×16 Words: Transformers for Image Recognition at Scale[EB/OL]. (2021-06-03)[2024-02-10]. https://arxiv.org/abs/2010.11929.
MA Q, JIANG J J, LIU X M, et al. Learning a 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution[J]. Information Fusion, 2023, 100: 101907. doi: 10.1016/j.inffus.2023.101907
FAROOQUE G, LIU Q C, SARGANO A B, et al. Swin Transformer with Multiscale 3D Atrous Convolution for Hyperspectral Image Classification[J]. Engineering Applications of Artificial Intelligence, 2023, 126: 107070. doi: 10.1016/j.engappai.2023.107070
LIU Z, LIN Y T, CAO Y, et al. Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows[C] //2021 IEEE/CVF International Conference on Computer Vision (ICCV), October 10-17, 2021, Montreal, QC, Canada. IEEE, 2021: 9992-10002.
GAO L R, HAN Z, HONG D F, et al. CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5503914.
SU Y C, ZHU Z Q, GAO L R, et al. DAAN: A Deep Autoencoder-Based Augmented Network for Blind Multilinear Hyperspectral Unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5512715.
NASCIMENTO J M P, DIAS J M B. Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 898-910.