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An Iterative Pseudo Label Generation framework for semi-supervised hyperspectral image classification using the

Zheng Zhao¹, Guangyao Zhou², Qixiong Wang¹

¹School of Astronautics, Beihang University, Beijing, China.

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|January 7, 2025

Summary

This study introduces an Iterative Pseudo Label Generation (IPG) framework using the Segment Anything Model (SAM) to improve semi-supervised hyperspectral image classification. The method enhances classification accuracy by generating reliable pseudo labels from limited data.

Keywords:

Segment Anything Model hyperspectral image classification pseudo label generation remote sensing semi-supervised learning

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Area of Science:

Remote Sensing
Computer Vision
Machine Learning

Background:

Hyperspectral image classification faces challenges with limited annotated data.
Semi-supervised learning offers solutions but relies on high-quality pseudo labels.
Pseudo label quality is critical, especially early in training.

Purpose of the Study:

To propose an Iterative Pseudo Label Generation (IPG) framework for semi-supervised hyperspectral image classification.
To leverage structural prior information from the Segment Anything Model (SAM).
To enhance classification performance by generating reliable pseudo labels.

Main Methods:

Utilized a small set of annotated labels as SAM point prompts for initial segmentation masks.
Introduced a spectral voting strategy to unify segmentation masks across spectral bands.
Developed a spatial-information-consistency-driven loss function for adaptive pseudo label selection.
Employed iterative refinement of pseudo labels as SAM prompts.

Main Results:

The IPG framework effectively generates reliable pseudo labels for training.
A simple 2D CNN trained with IPG-generated labels significantly outperformed state-of-the-art methods.
Demonstrated improved performance on the Indian Pines and Pavia University datasets.

Conclusions:

The proposed IPG framework successfully addresses the challenge of limited annotated data in hyperspectral image classification.
Harnessing structural priors via SAM significantly boosts semi-supervised learning performance.
The method offers a robust approach to enrich training data and improve classification accuracy.