{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7ZESH7KSMZFCTWYJ5TYHGAW2SJ","short_pith_number":"pith:7ZESH7KS","schema_version":"1.0","canonical_sha256":"fe4923fd52664a29db09ecf07302da927a3afcbdaf161720c2dfb4bf35e53ed7","source":{"kind":"arxiv","id":"1905.06133","version":1},"attestation_state":"computed","paper":{"title":"Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"eess.IV","authors_text":"Bo Du, Chen Gong, Jian Yang, Lefei Zhang, Ping Zhong, Sheng Wan","submitted_at":"2019-05-14T14:27:37Z","abstract_excerpt":"Convolutional Neural Network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. However, traditional CNN models can only operate convolution on regular square image regions with fixed size and weights, so they cannot universally adapt to the distinct local regions with various object distributions and geometric appearances. Therefore, their classification performances are still to be improved, especially in class boundaries. To alleviate this shortcoming, we consider employing the recently proposed"},"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":"1905.06133","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-05-14T14:27:37Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"1c2e212226abb139e27d85cec40b7deb3b08a6cbdb282cd2cca317d9a8269afa","abstract_canon_sha256":"aacf5f65a03ec6879de27fd5876652fe62dc56fc565505e275457aedf0295baf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:07.365077Z","signature_b64":"cqDLneofLNJYDQOAMHVMWlW217vjzKqzbjEXp9xqLG3gMvuGd/mvF737bFFoXaA3qhaSnJ81zTYJ6js7hSlNCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe4923fd52664a29db09ecf07302da927a3afcbdaf161720c2dfb4bf35e53ed7","last_reissued_at":"2026-05-17T23:46:07.364553Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:07.364553Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"eess.IV","authors_text":"Bo Du, Chen Gong, Jian Yang, Lefei Zhang, Ping Zhong, Sheng Wan","submitted_at":"2019-05-14T14:27:37Z","abstract_excerpt":"Convolutional Neural Network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. However, traditional CNN models can only operate convolution on regular square image regions with fixed size and weights, so they cannot universally adapt to the distinct local regions with various object distributions and geometric appearances. Therefore, their classification performances are still to be improved, especially in class boundaries. To alleviate this shortcoming, we consider employing the recently proposed"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.06133","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":"1905.06133","created_at":"2026-05-17T23:46:07.364643+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.06133v1","created_at":"2026-05-17T23:46:07.364643+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.06133","created_at":"2026-05-17T23:46:07.364643+00:00"},{"alias_kind":"pith_short_12","alias_value":"7ZESH7KSMZFC","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"7ZESH7KSMZFCTWYJ","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"7ZESH7KS","created_at":"2026-05-18T12:33:12.712433+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/7ZESH7KSMZFCTWYJ5TYHGAW2SJ","json":"https://pith.science/pith/7ZESH7KSMZFCTWYJ5TYHGAW2SJ.json","graph_json":"https://pith.science/api/pith-number/7ZESH7KSMZFCTWYJ5TYHGAW2SJ/graph.json","events_json":"https://pith.science/api/pith-number/7ZESH7KSMZFCTWYJ5TYHGAW2SJ/events.json","paper":"https://pith.science/paper/7ZESH7KS"},"agent_actions":{"view_html":"https://pith.science/pith/7ZESH7KSMZFCTWYJ5TYHGAW2SJ","download_json":"https://pith.science/pith/7ZESH7KSMZFCTWYJ5TYHGAW2SJ.json","view_paper":"https://pith.science/paper/7ZESH7KS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.06133&json=true","fetch_graph":"https://pith.science/api/pith-number/7ZESH7KSMZFCTWYJ5TYHGAW2SJ/graph.json","fetch_events":"https://pith.science/api/pith-number/7ZESH7KSMZFCTWYJ5TYHGAW2SJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7ZESH7KSMZFCTWYJ5TYHGAW2SJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7ZESH7KSMZFCTWYJ5TYHGAW2SJ/action/storage_attestation","attest_author":"https://pith.science/pith/7ZESH7KSMZFCTWYJ5TYHGAW2SJ/action/author_attestation","sign_citation":"https://pith.science/pith/7ZESH7KSMZFCTWYJ5TYHGAW2SJ/action/citation_signature","submit_replication":"https://pith.science/pith/7ZESH7KSMZFCTWYJ5TYHGAW2SJ/action/replication_record"}},"created_at":"2026-05-17T23:46:07.364643+00:00","updated_at":"2026-05-17T23:46:07.364643+00:00"}