{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ARECYXBVQVHZ72WYPBPWF3RDQ2","short_pith_number":"pith:ARECYXBV","schema_version":"1.0","canonical_sha256":"04482c5c35854f9fead8785f62ee2386aa9a5b7f92ef062db41dad93a695ec44","source":{"kind":"arxiv","id":"2405.00951","version":1},"attestation_state":"computed","paper":{"title":"Hyperspectral Band Selection based on Generalized 3DTV and Tensor CUR Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA","math.OC"],"primary_cat":"cs.CV","authors_text":"Jing Qin, Katherine Henneberger","submitted_at":"2024-05-02T02:23:38Z","abstract_excerpt":"Hyperspectral Imaging (HSI) serves as an important technique in remote sensing. However, high dimensionality and data volume typically pose significant computational challenges. Band selection is essential for reducing spectral redundancy in hyperspectral imagery while retaining intrinsic critical information. In this work, we propose a novel hyperspectral band selection model by decomposing the data into a low-rank and smooth component and a sparse one. In particular, we develop a generalized 3D total variation (G3DTV) by applying the $\\ell_1^p$-norm to derivatives to preserve spatial-spectra"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2405.00951","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-02T02:23:38Z","cross_cats_sorted":["cs.NA","math.NA","math.OC"],"title_canon_sha256":"92a7116250fc380be5c8190f43e6339f5039fe2ee5224f855e651347256a3be8","abstract_canon_sha256":"c0e3eab3520838c3456010addf0a32f0c4a2ba45ab203ea3dbd3d9ab4211c92d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:28.427146Z","signature_b64":"dko6F3rt3qwBGjcRJfgRIZq8d+u72woDW8gWDFOHS11Pbev1MRtvP8aYzX+wsTDZaRyyi1sBiUjQKIR6JafeDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04482c5c35854f9fead8785f62ee2386aa9a5b7f92ef062db41dad93a695ec44","last_reissued_at":"2026-07-05T08:14:28.426680Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:28.426680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hyperspectral Band Selection based on Generalized 3DTV and Tensor CUR Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA","math.OC"],"primary_cat":"cs.CV","authors_text":"Jing Qin, Katherine Henneberger","submitted_at":"2024-05-02T02:23:38Z","abstract_excerpt":"Hyperspectral Imaging (HSI) serves as an important technique in remote sensing. However, high dimensionality and data volume typically pose significant computational challenges. Band selection is essential for reducing spectral redundancy in hyperspectral imagery while retaining intrinsic critical information. In this work, we propose a novel hyperspectral band selection model by decomposing the data into a low-rank and smooth component and a sparse one. In particular, we develop a generalized 3D total variation (G3DTV) by applying the $\\ell_1^p$-norm to derivatives to preserve spatial-spectra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.00951","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.00951/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2405.00951","created_at":"2026-07-05T08:14:28.426738+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.00951v1","created_at":"2026-07-05T08:14:28.426738+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.00951","created_at":"2026-07-05T08:14:28.426738+00:00"},{"alias_kind":"pith_short_12","alias_value":"ARECYXBVQVHZ","created_at":"2026-07-05T08:14:28.426738+00:00"},{"alias_kind":"pith_short_16","alias_value":"ARECYXBVQVHZ72WY","created_at":"2026-07-05T08:14:28.426738+00:00"},{"alias_kind":"pith_short_8","alias_value":"ARECYXBV","created_at":"2026-07-05T08:14:28.426738+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ARECYXBVQVHZ72WYPBPWF3RDQ2","json":"https://pith.science/pith/ARECYXBVQVHZ72WYPBPWF3RDQ2.json","graph_json":"https://pith.science/api/pith-number/ARECYXBVQVHZ72WYPBPWF3RDQ2/graph.json","events_json":"https://pith.science/api/pith-number/ARECYXBVQVHZ72WYPBPWF3RDQ2/events.json","paper":"https://pith.science/paper/ARECYXBV"},"agent_actions":{"view_html":"https://pith.science/pith/ARECYXBVQVHZ72WYPBPWF3RDQ2","download_json":"https://pith.science/pith/ARECYXBVQVHZ72WYPBPWF3RDQ2.json","view_paper":"https://pith.science/paper/ARECYXBV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.00951&json=true","fetch_graph":"https://pith.science/api/pith-number/ARECYXBVQVHZ72WYPBPWF3RDQ2/graph.json","fetch_events":"https://pith.science/api/pith-number/ARECYXBVQVHZ72WYPBPWF3RDQ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ARECYXBVQVHZ72WYPBPWF3RDQ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ARECYXBVQVHZ72WYPBPWF3RDQ2/action/storage_attestation","attest_author":"https://pith.science/pith/ARECYXBVQVHZ72WYPBPWF3RDQ2/action/author_attestation","sign_citation":"https://pith.science/pith/ARECYXBVQVHZ72WYPBPWF3RDQ2/action/citation_signature","submit_replication":"https://pith.science/pith/ARECYXBVQVHZ72WYPBPWF3RDQ2/action/replication_record"}},"created_at":"2026-07-05T08:14:28.426738+00:00","updated_at":"2026-07-05T08:14:28.426738+00:00"}