{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:32T7LGTOASHTN22NFPOYBWTPJP","short_pith_number":"pith:32T7LGTO","schema_version":"1.0","canonical_sha256":"dea7f59a6e048f36eb4d2bdd80da6f4bc517437aa5d97ea0e3fe04c4f53bd2cb","source":{"kind":"arxiv","id":"1711.07141","version":1},"attestation_state":"computed","paper":{"title":"Spectral-Spatial Feature Extraction and Classification by ANN Supervised with Center Loss in Hyperspectral Imagery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alan J.X. Guo, Fei Zhu","submitted_at":"2017-11-20T04:46:45Z","abstract_excerpt":"In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery. With limited labeled samples, only spectral information is exploited for training and spatial context is integrated posteriorly at the testing stage. Taking advantage of recent advances in face recognition, a joint supervision symbol that combines softmax loss and center loss is adopted to train the proposed network, by which intra-class features are gathered while inter-class variations are enlarged. Based on the learned"},"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":"1711.07141","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-20T04:46:45Z","cross_cats_sorted":[],"title_canon_sha256":"7f1bdc53a395d5b19e4bef018635cead72297797fe72fb980f2a796c5e2125bd","abstract_canon_sha256":"ebfcb117b6e4eda9d75beae6d18fab25ac8de6af21056ea5383a785903189354"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:12.124439Z","signature_b64":"AIYn+FLYOaf8CbW9xmWzfjDFhk+I+ZR2RePL8vMS6vFgvABf5Rjt1Q45kxxh+FhHUCwGBM/Hxd9RPmdagnvoDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dea7f59a6e048f36eb4d2bdd80da6f4bc517437aa5d97ea0e3fe04c4f53bd2cb","last_reissued_at":"2026-05-18T00:30:12.123779Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:12.123779Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spectral-Spatial Feature Extraction and Classification by ANN Supervised with Center Loss in Hyperspectral Imagery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alan J.X. Guo, Fei Zhu","submitted_at":"2017-11-20T04:46:45Z","abstract_excerpt":"In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery. With limited labeled samples, only spectral information is exploited for training and spatial context is integrated posteriorly at the testing stage. Taking advantage of recent advances in face recognition, a joint supervision symbol that combines softmax loss and center loss is adopted to train the proposed network, by which intra-class features are gathered while inter-class variations are enlarged. Based on the learned"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.07141","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":""},"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":"1711.07141","created_at":"2026-05-18T00:30:12.123883+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.07141v1","created_at":"2026-05-18T00:30:12.123883+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.07141","created_at":"2026-05-18T00:30:12.123883+00:00"},{"alias_kind":"pith_short_12","alias_value":"32T7LGTOASHT","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"32T7LGTOASHTN22N","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"32T7LGTO","created_at":"2026-05-18T12:30:55.937587+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/32T7LGTOASHTN22NFPOYBWTPJP","json":"https://pith.science/pith/32T7LGTOASHTN22NFPOYBWTPJP.json","graph_json":"https://pith.science/api/pith-number/32T7LGTOASHTN22NFPOYBWTPJP/graph.json","events_json":"https://pith.science/api/pith-number/32T7LGTOASHTN22NFPOYBWTPJP/events.json","paper":"https://pith.science/paper/32T7LGTO"},"agent_actions":{"view_html":"https://pith.science/pith/32T7LGTOASHTN22NFPOYBWTPJP","download_json":"https://pith.science/pith/32T7LGTOASHTN22NFPOYBWTPJP.json","view_paper":"https://pith.science/paper/32T7LGTO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.07141&json=true","fetch_graph":"https://pith.science/api/pith-number/32T7LGTOASHTN22NFPOYBWTPJP/graph.json","fetch_events":"https://pith.science/api/pith-number/32T7LGTOASHTN22NFPOYBWTPJP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/32T7LGTOASHTN22NFPOYBWTPJP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/32T7LGTOASHTN22NFPOYBWTPJP/action/storage_attestation","attest_author":"https://pith.science/pith/32T7LGTOASHTN22NFPOYBWTPJP/action/author_attestation","sign_citation":"https://pith.science/pith/32T7LGTOASHTN22NFPOYBWTPJP/action/citation_signature","submit_replication":"https://pith.science/pith/32T7LGTOASHTN22NFPOYBWTPJP/action/replication_record"}},"created_at":"2026-05-18T00:30:12.123883+00:00","updated_at":"2026-05-18T00:30:12.123883+00:00"}