A project exploring different CNN architectures (1D, 2D, 3D, and Hybrid) for hyperspectral image classification using the Pavia University dataset. Project Overview This repository contains ...

Though both methods gave decent performance in distinguishing for healthy and MCI, but, classification accuracy obtained using inter-ROI Wasserstein distance between persistent diagrams obtained using ...

Graph convolutional networks (GCNs) exhibit remarkable capabilities in hyperspectral image (HSI) classification tasks, primarily due to their ability to establish long-range pixel correlations. This ...

Research team led by Chuliang Weng introduces D2-GCN, a groundbreaking disentangled graph convolutional network that dynamically adjusts feature channels for enhanced node representation ...

Existing few-shot image classification networks aim to perform prediction on images belonging to classes that were not seen during training, with only a few labeled images, which are randomly picked ...