Reconstruction-Contrast Coupling Learning for Open-Set Semi-Supervised Hyperspectral Image Classification | JoVE Visualize

Area of Science:

Remote Sensing
Computer Vision
Machine Learning

Background:

Existing semi-supervised hyperspectral image (HSI) classification methods often assume a closed-set, where all data belong to known categories.
This closed-set assumption is unrealistic in remote sensing due to the frequent presence of unknown categories in unlabeled data.
This limitation hinders the practical application of HSI classification in real-world scenarios.

Purpose of the Study:

To propose a novel open-set semi-supervised learning method for HSI classification that addresses the challenge of unknown categories.
To leverage the synergy between masked feature reconstruction and contrastive learning for improved feature representation.
To enhance the encoder's ability to capture both local spectral-spatial details and global semantic information.

Main Methods:

Introduced Reconstruction-Contrast Coupling Learning (ReCo2L) for open-set semi-supervised HSI classification.
Employed masked feature reconstruction with an adaptive masking strategy to improve local detail sensitivity.
Utilized contrastive learning to enhance global feature discriminative ability and introduced a pixel-prototype deviation loss for better category separation.

Main Results:

ReCo2L demonstrated superior classification performance on three benchmark datasets, effectively classifying both known and unknown categories.
The proposed method significantly outperformed 10 state-of-the-art HSI classification techniques.
The combination of reconstruction and contrastive learning proved effective in enhancing feature extraction for HSI data.

Conclusions:

The ReCo2L method offers a robust solution for open-set semi-supervised HSI classification, overcoming the limitations of closed-set assumptions.
The approach effectively enhances spectral-spatial feature representation by integrating reconstruction and contrastive learning objectives.
The findings highlight the potential of ReCo2L for practical remote sensing applications requiring accurate classification of diverse land cover types.