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Deep Learning Hyperspectral Image Classification using Multiple Class-Based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations., and . IGARSS, page 6903-6906. IEEE, (2018)CLODD based band group selection., , , and . IGARSS, page 36-39. IEEE, (2016)A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community., , and . CoRR, (2017)Fusion of diverse features and kernels using LP-norm based multiple kernel learning in hyperspectral image processing., , , and . WHISPERS, page 1-5. IEEE, (2016)Closing the Wearable Gap - Part V: Development of a Pressure-Sensitive Sock Utilizing Soft Sensors., , , , , , , , and . Sensors, 20 (1): 208 (2020)Closing the Wearable Gap - Part II: Sensor Orientation and Placement for Foot and Ankle Joint Kinematic Measurements., , , , , , , , , and 1 other author(s). Sensors, 19 (16): 3509 (2019)Engaging Students in an Automotive Autonomy Sensor Processing Class: Incorporating active learning and high-fidelity, physics-based autonomy simulation into class projects., , and . IEEE Signal Process. Mag., 38 (3): 122-132 (2021)Onion-Peeling Outlier Detection in 2-D data Sets., , and . CoRR, (2018)Fusion of heterogeneous bands and kernels in hyperspectral image processing., , , and . CoRR, (2019)Level Set Hyperspectral Image Classification Using Best Band Analysis., and . IEEE Trans. Geosci. Remote. Sens., 45 (10): 3022-3027 (2007)