部分已发表论文: Chen Q , Li Z, Yin J,et al, Local-Global Feature Extraction Network With Dynamic 3-D Convolution and Residual Attention Transformer for Hyperspectral Image Classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025(18): 9986-10001. Huang W, LI J, Chen Q, et al. SFCFNet: A Spatial–Frequency Cross-Attention Fusion Network for Hyperspectral Image Classification[J],IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2026(19): 4994-5008. Wu Q, He M, Chen Q, et al. Integrating Multiscale Spatial–Spectral Shuffling Convolution With 3-D Lightweight Transformer for Hyperspectral Image Classification[J], IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025(18): 5378-5394.
[4] Chen Q , Gan X , Huang W ,et al,.Road Damage Detection and Classification Using Mask R-CNN with DenseNet Backbone[J]. Computers, Materials & Continua, 2020(12): 2201-2215. [5] Huang W, Ju M, Chen Q, et al.Detail-injection-based multiscale asymmetric residual network for pansharpening[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5. [6] Yin J, Qu J, Chen Q, et al.Differential strategy-based multi-level dense network for pansharpening[J]. Remote Sensing, 2022, 14(10): 2347. [7] Huang W, Li G, Chen Q, et al. CF2PN: A cross-scale feature fusion pyramid network based remote sensing target detection[J]. Remote Sensing, 2021, 13(5): 847. [8] Yin J, Qi C, Chen Q, et al. Spatial-spectral network for hyperspectral image classification: A 3-D CNN and Bi-LSTM framework[J]. Remote Sensing, 2021, 13(12): 2353. 科研获奖: 河南省科技进步奖二等奖(2024年度),排名第七。 河南省科技进步奖三等奖(2020年度),排名第六。 部分授权专利: (1)陈启强,殷君茹,张明霞等,耕地整治潜力评价方法、终端以及计算机可读存储介质,ZL 2021 1 0541414.5。 (2)南姣芬,陈启强 ,朱颢东等,基于主成分分析的关键脑区的度量方法,ZL 2017 1 0088076.8。 (3)万毅,杜厚庆,陈启强,-种新的基于两条相交直线的相机标定方法,CN201210249333.9。 |