Semi-supervised Deep Convolutional Transform Learning for Hyperspectral Image Classification

Shikha Singh (1) , Angshul Majumdar (1) , Emilie Chouzenoux (2) , Giovanni Chierchia (3, 4)

1 IIIT-Delhi - Indraprastha Institute of Information Technology [New Delhi]
2 OPIS - OPtimisation Imagerie et Santé
3 ESIEE Paris
4 LIGM - Laboratoire d'Informatique Gaspard-Monge

This work addresses the problem of hyperspectral image classification when the number of labeled samples is very small (few shot learning). Our work is based on the recently proposed framework of convolutional transform learning. In this work, we propose a semisupervised version of deep convolutional transform learning. We compare with four recent studies which are tailored for solving the few-shot learning problem in hyperspectral classification. Results show that our method can improve over the state-of-the-art.

Mots clés: hyperspectral classification; supervised learning; deep learning

Domaines: Traitement du signal et de l'image [eess.SP]

Shikha Singh, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia. Semi-supervised Deep Convolutional Transform Learning for Hyperspectral Image Classification. ICIP 2022 - 29th IEEE International Conference on Image Processing, Oct 2022, Bordeaux, France. ⟨hal-03723462⟩