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View all- Amoako PKyei E(2024)Deep Learning Models for Small Sample Hyperspectral Image Classification2024 IEEE SmartBlock4Africa10.1109/SmartBlock4Africa61928.2024.10779498(1-13)Online publication date: 30-Sep-2024
Hyperspectral image(HSI) classification is a crucial topic within remote sensing. Recently, deep self-supervised learning methods have gained widespread use in HSI classification, effectively addressing the scarcity of labeled samples issue. In ...
Few-Shot Image Classification (FSIC) aims to learn an image classifier with only a few training samples. The key challenge of few-shot image classification is to learn this classifier with scarce labeled data. To tackle the issue, we leverage the ...
Recently, collaborative learning (CL) is introduced to combine active learning (AL) with semi-supervised learning (SSL), and solve the problem of limited training samples. In this paper, we proposed a novel CL framework for hyperspectral image ...
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