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SEMI-SUPERVISED GRAPHICAL DEEP DICTIONARY LEARNING FOR HYPERSPECTRAL IMAGE CLASSIFICATION FROM LIMITED SAMPLES

Citation Author(s):
Anurag Goel, Angshul Majumdar
Submitted by:
Anurag Goel
Last updated:
8 November 2024 - 10:14am
Document Type:
Poster
Document Year:
2024
Event:
Presenters:
Anurag Goel
Paper Code:
1202
Categories:
Keywords:
 

In this work, we propose a semi-supervised deep feature generation network that accounts for local similarities. It is based on the deep dictionary learning (DDL) framework. The formulation accounts for two unique aspects of hyperspectral classification. First, the fact that the total number of pixels / samples to be labeled is constant; this allows for a semi-supervised formulation allowing only a few pixels / samples to be labeled as training data. Second, the samples / pixels are spatially correlated; this leads to a graph regularization formulation. Our formulation has been benchmarked with state-of-the-art techniques on two popular datasets; the results show that our work improves upon the ones compared against.

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