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Found 6 papers, 1 papers with code
Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers
Few-shot knowledge distillation recently emerged as a viable approach to harness the knowledge of large-scale pre-trained models, using limited data and computational resources.
Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends
Recent advancements in deep learning techniques have spurred considerable interest in their application to hyperspectral imagery processing.
Deep Learning for In-Orbit Cloud Segmentation and Classification in Hyperspectral Satellite Data
The performance of the latest 1D CNN (1D-Justo-LiuNet) and two recent 2D CNNs (nnU-net and 2D-Justo-UNet-Simple) for cloud segmentation and classification is assessed.
Study of the gOMP Algorithm for Recovery of Compressed Sensed Hyperspectral Images
It is concluded that the gOMP algorithm reconstructs the hyperspectral images with higher accuracy as well as faster convergence when the pixels are highly sparsified and hence at the expense of reducing the quality of the recovered images with respect to the original images.
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction
Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission.
Sea-Land-Cloud Segmentation in Satellite Hyperspectral Imagery by Deep Learning
Satellites are increasingly adopting on-board AI for enhanced autonomy through in-orbit inference.
