{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-metric-learning-based-feature-embedding","title":"Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification","arxiv_id":null,"date":"2019-10-30","proceeding":"IEEE Transactions on Geoscience and Remote Sensing 2019 10","authors":["Bin Deng","Sen Jia","Daming Shi"],"abstract":"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.","url_abs":"https://doi.org/10.1109/TGRS.2019.2946318","url_pdf":"https://doi.org/10.1109/TGRS.2019.2946318","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-metric-learning-based-feature-embedding","repo_url":"https://github.com/szubing/S-DMM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"hyperspectral-image-classification","task_name":"Hyperspectral Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"scene-classification","task_name":"Scene Classification"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hyperspectral-image-classification-on-indian","task":"Hyperspectral Image Classification","dataset":"Indian Pines","model":"S-DMM","rank_in_archive_order":10,"of":34,"metrics":{"OA@15perclass":"67.04±1.65"},"uses_additional_data":false},{"leaderboard":"/sota/hyperspectral-image-classification-on-kennedy","task":"Hyperspectral Image Classification","dataset":"Kennedy Space Center","model":"S-DMM","rank_in_archive_order":4,"of":14,"metrics":{"OA@15perclass":"95.83±1.68"},"uses_additional_data":false},{"leaderboard":"/sota/hyperspectral-image-classification-on-pavia","task":"Hyperspectral Image Classification","dataset":"Pavia University","model":"S-DMM","rank_in_archive_order":6,"of":33,"metrics":{"OA@15perclass":"88.30±1.03"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}