{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DHIBWBEVW2KZPZIJ3U7D3SJUT2","short_pith_number":"pith:DHIBWBEV","schema_version":"1.0","canonical_sha256":"19d01b0495b69597e509dd3e3dc9349e9f1efaac385bb5d2e83d3578bf1a497e","source":{"kind":"arxiv","id":"1802.10478","version":1},"attestation_state":"computed","paper":{"title":"HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengfei Yao, Gang Bai, Jie Zou, Tao Li, Yanan Luo","submitted_at":"2018-02-28T15:31:20Z","abstract_excerpt":"With the development of deep learning, the performance of hyperspectral image (HSI) classification has been greatly improved in recent years. The shortage of training samples has become a bottleneck for further improvement of performance. In this paper, we propose a novel convolutional neural network framework for the characteristics of hyperspectral image data, called HSI-CNN. Firstly, the spectral-spatial feature is extracted from a target pixel and its neighbors. Then, a number of one-dimensional feature maps, obtained by convolution operation on spectral-spatial features, are stacked into "},"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":"1802.10478","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-28T15:31:20Z","cross_cats_sorted":[],"title_canon_sha256":"7387807d044866b205f0cc1ceec7d879da8ae361d79b271977db3bc73d98c0b1","abstract_canon_sha256":"ea921ba7063ebd95d93c988c99f5646ab5162d016b8e9b0f03874f911680c7b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:17.046847Z","signature_b64":"gW9I7l3sFjmlebDW1Pe4e5MoGKRA5LBPtDv5hEMfEutzCshn1P5IJVP6E7S0G1mwIPlWP4ZMte9Mzqz9rZu2Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"19d01b0495b69597e509dd3e3dc9349e9f1efaac385bb5d2e83d3578bf1a497e","last_reissued_at":"2026-05-18T00:22:17.046220Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:17.046220Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengfei Yao, Gang Bai, Jie Zou, Tao Li, Yanan Luo","submitted_at":"2018-02-28T15:31:20Z","abstract_excerpt":"With the development of deep learning, the performance of hyperspectral image (HSI) classification has been greatly improved in recent years. The shortage of training samples has become a bottleneck for further improvement of performance. In this paper, we propose a novel convolutional neural network framework for the characteristics of hyperspectral image data, called HSI-CNN. Firstly, the spectral-spatial feature is extracted from a target pixel and its neighbors. Then, a number of one-dimensional feature maps, obtained by convolution operation on spectral-spatial features, are stacked into "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.10478","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":"1802.10478","created_at":"2026-05-18T00:22:17.046326+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.10478v1","created_at":"2026-05-18T00:22:17.046326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.10478","created_at":"2026-05-18T00:22:17.046326+00:00"},{"alias_kind":"pith_short_12","alias_value":"DHIBWBEVW2KZ","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DHIBWBEVW2KZPZIJ","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DHIBWBEV","created_at":"2026-05-18T12:32:19.392346+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/DHIBWBEVW2KZPZIJ3U7D3SJUT2","json":"https://pith.science/pith/DHIBWBEVW2KZPZIJ3U7D3SJUT2.json","graph_json":"https://pith.science/api/pith-number/DHIBWBEVW2KZPZIJ3U7D3SJUT2/graph.json","events_json":"https://pith.science/api/pith-number/DHIBWBEVW2KZPZIJ3U7D3SJUT2/events.json","paper":"https://pith.science/paper/DHIBWBEV"},"agent_actions":{"view_html":"https://pith.science/pith/DHIBWBEVW2KZPZIJ3U7D3SJUT2","download_json":"https://pith.science/pith/DHIBWBEVW2KZPZIJ3U7D3SJUT2.json","view_paper":"https://pith.science/paper/DHIBWBEV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.10478&json=true","fetch_graph":"https://pith.science/api/pith-number/DHIBWBEVW2KZPZIJ3U7D3SJUT2/graph.json","fetch_events":"https://pith.science/api/pith-number/DHIBWBEVW2KZPZIJ3U7D3SJUT2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DHIBWBEVW2KZPZIJ3U7D3SJUT2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DHIBWBEVW2KZPZIJ3U7D3SJUT2/action/storage_attestation","attest_author":"https://pith.science/pith/DHIBWBEVW2KZPZIJ3U7D3SJUT2/action/author_attestation","sign_citation":"https://pith.science/pith/DHIBWBEVW2KZPZIJ3U7D3SJUT2/action/citation_signature","submit_replication":"https://pith.science/pith/DHIBWBEVW2KZPZIJ3U7D3SJUT2/action/replication_record"}},"created_at":"2026-05-18T00:22:17.046326+00:00","updated_at":"2026-05-18T00:22:17.046326+00:00"}