Papers › Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification

Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification

30 Oct 2019IEEE Transactions on Geoscience and Remote Sensing 2019 10archive 2025-07-28

Bin Deng, Sen Jia, Daming Shi

Learning from a limited number of labeled samples (pixels) remains a key challenge in the hyperspectral image (HSI) classification. To address this issue, we propose a deep metric learning-based feature embedding model, which can meet the tasks both for same- and cross-scene HSI classifications. In the first task, when only a few labeled samples are available, we employ ideas from metric learning based on deep embedding features and make a similarity learning between pairs of samples. In this case, the proposed model can learn well to compare whether two samples belong to the same class. In another task, when an HSI image (target scene) that needs to be classified is not labeled at all, the embedding model can learn from another similar HSI image (source scene) with sufficient labeled samples and then transfer to the target model by using an unsupervised domain adaptation technique, which not only employs the adversarial approach to make the embedding features from the source and target samples indistinguishable but also encourages the target scene's embeddings to form similar clusters with the source scene one. After the domain adaptation between the HSIs of the two scenes is finished, any traditional HSI classifier can be used. In a simple manner, the nearest neighbor (NN) algorithm is selected as the classifier for the classification tasks throughout this article. The experimental results from a series of popular HSIs demonstrate the advantages of the proposed model both in the same- and cross-scene classification tasks.

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Tasks

Domain Adaptation Few-Shot Image Classification Hyperspectral Image Classification Image Classification Metric Learning Scene Classification Unsupervised Domain Adaptation image-classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Hyperspectral Image Classification	Indian Pines	S-DMM	OA@15perclass	67.04±1.65	#10 of 34	Archive leaderboard	report
Hyperspectral Image Classification	Kennedy Space Center	S-DMM	OA@15perclass	95.83±1.68	#4 of 14	Archive leaderboard	report
Hyperspectral Image Classification	Pavia University	S-DMM	OA@15perclass	88.30±1.03	#6 of 33	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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