{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:WHS2N6LS2UAHUFF5GBO4ETQST7","short_pith_number":"pith:WHS2N6LS","schema_version":"1.0","canonical_sha256":"b1e5a6f972d5007a14bd305dc24e129fc78ac434d1fae051bbb43fa8d4724701","source":{"kind":"arxiv","id":"1711.05512","version":4},"attestation_state":"computed","paper":{"title":"Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Elena Marchiori, Jacopo Acquarelli, Lutgarde M.C. Buydens, Thanh Tran, Twan van Laarhoven","submitted_at":"2017-11-15T12:02:57Z","abstract_excerpt":"Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. In the presence of only very few labeled pixels, this task becomes challenging. In this paper we address the following two research questions: 1) Can a simple neural network with just a single hidden layer achieve state of the art performance in the presence of few labeled pixels? 2) How is the performance of hyperspectral image classification methods affected when using disjoint train and test sets? We give a positive answer to the first question by using three tricks within a very ba"},"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":"1711.05512","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-15T12:02:57Z","cross_cats_sorted":[],"title_canon_sha256":"1d56966f41ab0b5f3730428cd392b42c87e4e7467e8d1878158a194ae550eba9","abstract_canon_sha256":"ec005fa1aa4d855e722f72c9ebb0f5e5c90de49b8691b0fce96562235115ca14"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:11.069408Z","signature_b64":"zjsRxBJ0pofiSgBELhVu3NH7EzYpE4BR4DKcXEbYHC8nee4Bb0l8RXzvJYy4G6rD9PoF93DPygWJD0bQ4bjUAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1e5a6f972d5007a14bd305dc24e129fc78ac434d1fae051bbb43fa8d4724701","last_reissued_at":"2026-05-18T00:10:11.068592Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:11.068592Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Elena Marchiori, Jacopo Acquarelli, Lutgarde M.C. Buydens, Thanh Tran, Twan van Laarhoven","submitted_at":"2017-11-15T12:02:57Z","abstract_excerpt":"Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. In the presence of only very few labeled pixels, this task becomes challenging. In this paper we address the following two research questions: 1) Can a simple neural network with just a single hidden layer achieve state of the art performance in the presence of few labeled pixels? 2) How is the performance of hyperspectral image classification methods affected when using disjoint train and test sets? We give a positive answer to the first question by using three tricks within a very ba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05512","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1711.05512","created_at":"2026-05-18T00:10:11.068724+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.05512v4","created_at":"2026-05-18T00:10:11.068724+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05512","created_at":"2026-05-18T00:10:11.068724+00:00"},{"alias_kind":"pith_short_12","alias_value":"WHS2N6LS2UAH","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_16","alias_value":"WHS2N6LS2UAHUFF5","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_8","alias_value":"WHS2N6LS","created_at":"2026-05-18T12:31:53.515858+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/WHS2N6LS2UAHUFF5GBO4ETQST7","json":"https://pith.science/pith/WHS2N6LS2UAHUFF5GBO4ETQST7.json","graph_json":"https://pith.science/api/pith-number/WHS2N6LS2UAHUFF5GBO4ETQST7/graph.json","events_json":"https://pith.science/api/pith-number/WHS2N6LS2UAHUFF5GBO4ETQST7/events.json","paper":"https://pith.science/paper/WHS2N6LS"},"agent_actions":{"view_html":"https://pith.science/pith/WHS2N6LS2UAHUFF5GBO4ETQST7","download_json":"https://pith.science/pith/WHS2N6LS2UAHUFF5GBO4ETQST7.json","view_paper":"https://pith.science/paper/WHS2N6LS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.05512&json=true","fetch_graph":"https://pith.science/api/pith-number/WHS2N6LS2UAHUFF5GBO4ETQST7/graph.json","fetch_events":"https://pith.science/api/pith-number/WHS2N6LS2UAHUFF5GBO4ETQST7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WHS2N6LS2UAHUFF5GBO4ETQST7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WHS2N6LS2UAHUFF5GBO4ETQST7/action/storage_attestation","attest_author":"https://pith.science/pith/WHS2N6LS2UAHUFF5GBO4ETQST7/action/author_attestation","sign_citation":"https://pith.science/pith/WHS2N6LS2UAHUFF5GBO4ETQST7/action/citation_signature","submit_replication":"https://pith.science/pith/WHS2N6LS2UAHUFF5GBO4ETQST7/action/replication_record"}},"created_at":"2026-05-18T00:10:11.068724+00:00","updated_at":"2026-05-18T00:10:11.068724+00:00"}