{"doi":"10.3390/rs9101074","title":"Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing","abstract":"<jats:p>Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing.     L  1 / 2      and     L 2     regularizers can be added to NMF to enforce sparseness and evenness, respectively. In practice, a region in a hyperspectral image may possess different sparsity levels across locations. The problem remains as to how to impose constraints accordingly when the level of sparsity varies. We propose a novel nonnegative matrix factorization with data-guided constraints (DGC-NMF). The DGC-NMF imposes on the unknown abundance vector of each pixel with either an     L  1 / 2      constraint or an     L 2     constraint according to its estimated mixture level. Experiments on the synthetic data and real hyperspectral data validate the proposed algorithm.</jats:p>","journal":"Remote Sensing","year":2017,"id":45529,"datarank":0.7724987168227104,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"self_citation_contribution":0.38474240361923057,"citation_network_contribution":0.3877563132034798,"self_endowment_contribution":0.38474240361923057,"citer_contribution":0.3877563132034798,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":9,"citers_with_citation_signal":7,"citers_with_endowment":7,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":212559,"name":"Xiaorun Li","orcid":"0000-0001-7611-845X","position":1,"is_corresponding":false},{"id":212560,"name":"Liaoying Zhao","orcid":"0000-0002-9276-8679","position":2,"is_corresponding":false},{"id":212558,"name":"Risheng Huang","orcid":"0000-0001-6661-023X","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21071399","pmcid":null,"openalex_id":"https://openalex.org/W2766940489","authors":[],"funders":[{"funder_name":"National Nature Science Foundation of China","grant_id":"61571170","title":null},{"funder_name":"National Nature Science Foundation of China","grant_id":"61671408","title":null},{"funder_name":"Joint Funds of the Ministry of Education of China","grant_id":"6141A02022314","title":null}],"total_grants":3,"fwci":1.4053,"citation_percentile":0.85804089,"influential_citations":0,"citation_trend":[{"year":2018,"count":3},{"year":2019,"count":1},{"year":2020,"count":2},{"year":2022,"count":3},{"year":2023,"count":1},{"year":2025,"count":1}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/2072-4292/9/10/1074/pdf?version=1508582427","host_type":"journal"},{"url":"https://www.mdpi.com/2072-4292/9/10/1074/pdf?version=1508582427","host_type":"publisher"},{"url":"https://www.mdpi.com/2072-4292/9/10/1074/pdf","host_type":"publisher"},{"url":"https://doi.org/10.3390/rs9101074","host_type":"journal"},{"url":"https://doaj.org/article/262f9463254848bd91a241eec0cc7a74","host_type":"repository"},{"url":"https://dx.doi.org/10.3390/rs9101074","host_type":"repository"}],"fields_of_study":["Remote-Sensing Image Classification","Advanced Image Fusion Techniques","Remote Sensing and Land Use"],"mesh_terms":[],"keywords":["Hyperspectral imaging","Non-negative matrix factorization","Endmember","Constraint (computer-aided design)","Pattern recognition (psychology)","Pixel","Computer science","Abundance estimation","Artificial intelligence","Set (abstract data type)","Matrix decomposition","Data set","Image (mathematics)","Mathematics","Abundance (ecology)","Biology"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-03T21:37:40.150925Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}