{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:OH2PNNZMMGLSXOOOIVCT2R3JAH","short_pith_number":"pith:OH2PNNZM","canonical_record":{"source":{"id":"1906.09631","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-23T19:11:26Z","cross_cats_sorted":[],"title_canon_sha256":"3f5029efdcff29aac4f2a35da69dcf17201ff1a9c9303a5caba0099f995b3ef0","abstract_canon_sha256":"d09253df6a5f5e582746cbb454e8d83144e6d0b3607f2472db9f225c07a7fa2b"},"schema_version":"1.0"},"canonical_sha256":"71f4f6b72c61972bb9ce45453d476901c1cf242bf0729a9d54b2110e08302065","source":{"kind":"arxiv","id":"1906.09631","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.09631","created_at":"2026-07-05T01:18:36Z"},{"alias_kind":"arxiv_version","alias_value":"1906.09631v1","created_at":"2026-07-05T01:18:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.09631","created_at":"2026-07-05T01:18:36Z"},{"alias_kind":"pith_short_12","alias_value":"OH2PNNZMMGLS","created_at":"2026-07-05T01:18:36Z"},{"alias_kind":"pith_short_16","alias_value":"OH2PNNZMMGLSXOOO","created_at":"2026-07-05T01:18:36Z"},{"alias_kind":"pith_short_8","alias_value":"OH2PNNZM","created_at":"2026-07-05T01:18:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:OH2PNNZMMGLSXOOOIVCT2R3JAH","target":"record","payload":{"canonical_record":{"source":{"id":"1906.09631","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-23T19:11:26Z","cross_cats_sorted":[],"title_canon_sha256":"3f5029efdcff29aac4f2a35da69dcf17201ff1a9c9303a5caba0099f995b3ef0","abstract_canon_sha256":"d09253df6a5f5e582746cbb454e8d83144e6d0b3607f2472db9f225c07a7fa2b"},"schema_version":"1.0"},"canonical_sha256":"71f4f6b72c61972bb9ce45453d476901c1cf242bf0729a9d54b2110e08302065","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:18:36.434936Z","signature_b64":"X/8gOEEHeCXT3hh9NY1vNBIV6YKEi9LZ1nAlTqwOEdWBRDEkxmQkaDhkIr3GytwubZqh3tdbD0NG0q/WGXhOAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71f4f6b72c61972bb9ce45453d476901c1cf242bf0729a9d54b2110e08302065","last_reissued_at":"2026-07-05T01:18:36.434536Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:18:36.434536Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.09631","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:18:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DJcxcuuy2nYf18E6UUtpmVGft6vdlW/p85gdKTOkOSkXBVNsF4xMQe6rdxi412HzNcmReiNFSybKACvhdGZ7DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T08:19:51.315990Z"},"content_sha256":"c9302977f5514e3bed2c3c1f031660349f3e14416e245dd26df0eb56b687524f","schema_version":"1.0","event_id":"sha256:c9302977f5514e3bed2c3c1f031660349f3e14416e245dd26df0eb56b687524f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:OH2PNNZMMGLSXOOOIVCT2R3JAH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jakub Nalepa, Michal Kawulok, Michal Myller","submitted_at":"2019-06-23T19:11:26Z","abstract_excerpt":"Deep learning has established the state of the art in multiple fields, including hyperspectral image analysis. However, training large-capacity learners to segment such imagery requires representative training sets. Acquiring such data is human-dependent and time-consuming, especially in Earth observation scenarios, where the hyperspectral data transfer is very costly and time-constrained. In this letter, we show how to effectively deal with a limited number and size of available hyperspectral ground-truth sets, and apply transfer learning for building deep feature extractors. Also, we exploit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.09631","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/1906.09631/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:18:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D3/pVjrPKM2+Pb1g3GmRMkhnhUjbyFhRUPX5mcRcpsPnSiXBv96v3m6e/xOj/8o/S2bnfm8+owcmuR5/00esBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T08:19:51.316509Z"},"content_sha256":"9fcffdca1c30e2ff688665ca67eba83de9141cb834b0088af962412d098af685","schema_version":"1.0","event_id":"sha256:9fcffdca1c30e2ff688665ca67eba83de9141cb834b0088af962412d098af685"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OH2PNNZMMGLSXOOOIVCT2R3JAH/bundle.json","state_url":"https://pith.science/pith/OH2PNNZMMGLSXOOOIVCT2R3JAH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OH2PNNZMMGLSXOOOIVCT2R3JAH/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T08:19:51Z","links":{"resolver":"https://pith.science/pith/OH2PNNZMMGLSXOOOIVCT2R3JAH","bundle":"https://pith.science/pith/OH2PNNZMMGLSXOOOIVCT2R3JAH/bundle.json","state":"https://pith.science/pith/OH2PNNZMMGLSXOOOIVCT2R3JAH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OH2PNNZMMGLSXOOOIVCT2R3JAH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:OH2PNNZMMGLSXOOOIVCT2R3JAH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d09253df6a5f5e582746cbb454e8d83144e6d0b3607f2472db9f225c07a7fa2b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-23T19:11:26Z","title_canon_sha256":"3f5029efdcff29aac4f2a35da69dcf17201ff1a9c9303a5caba0099f995b3ef0"},"schema_version":"1.0","source":{"id":"1906.09631","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.09631","created_at":"2026-07-05T01:18:36Z"},{"alias_kind":"arxiv_version","alias_value":"1906.09631v1","created_at":"2026-07-05T01:18:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.09631","created_at":"2026-07-05T01:18:36Z"},{"alias_kind":"pith_short_12","alias_value":"OH2PNNZMMGLS","created_at":"2026-07-05T01:18:36Z"},{"alias_kind":"pith_short_16","alias_value":"OH2PNNZMMGLSXOOO","created_at":"2026-07-05T01:18:36Z"},{"alias_kind":"pith_short_8","alias_value":"OH2PNNZM","created_at":"2026-07-05T01:18:36Z"}],"graph_snapshots":[{"event_id":"sha256:9fcffdca1c30e2ff688665ca67eba83de9141cb834b0088af962412d098af685","target":"graph","created_at":"2026-07-05T01:18:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1906.09631/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning has established the state of the art in multiple fields, including hyperspectral image analysis. However, training large-capacity learners to segment such imagery requires representative training sets. Acquiring such data is human-dependent and time-consuming, especially in Earth observation scenarios, where the hyperspectral data transfer is very costly and time-constrained. In this letter, we show how to effectively deal with a limited number and size of available hyperspectral ground-truth sets, and apply transfer learning for building deep feature extractors. Also, we exploit","authors_text":"Jakub Nalepa, Michal Kawulok, Michal Myller","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-23T19:11:26Z","title":"Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.09631","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c9302977f5514e3bed2c3c1f031660349f3e14416e245dd26df0eb56b687524f","target":"record","created_at":"2026-07-05T01:18:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d09253df6a5f5e582746cbb454e8d83144e6d0b3607f2472db9f225c07a7fa2b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-23T19:11:26Z","title_canon_sha256":"3f5029efdcff29aac4f2a35da69dcf17201ff1a9c9303a5caba0099f995b3ef0"},"schema_version":"1.0","source":{"id":"1906.09631","kind":"arxiv","version":1}},"canonical_sha256":"71f4f6b72c61972bb9ce45453d476901c1cf242bf0729a9d54b2110e08302065","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"71f4f6b72c61972bb9ce45453d476901c1cf242bf0729a9d54b2110e08302065","first_computed_at":"2026-07-05T01:18:36.434536Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:18:36.434536Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"X/8gOEEHeCXT3hh9NY1vNBIV6YKEi9LZ1nAlTqwOEdWBRDEkxmQkaDhkIr3GytwubZqh3tdbD0NG0q/WGXhOAA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:18:36.434936Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.09631","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c9302977f5514e3bed2c3c1f031660349f3e14416e245dd26df0eb56b687524f","sha256:9fcffdca1c30e2ff688665ca67eba83de9141cb834b0088af962412d098af685"],"state_sha256":"03b0395bd2a9f7736ad4d36391b470398239034d93c58c4f2b4c19edc4a4ddae"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tOCpX10ppRwLhvfFGxeFsx/PMF3is0f3A2EGd7bLq1ANbUf8e9W3Jpk9fz+275LpL0VyR1ydcP0zoY6lvj/BAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T08:19:51.320248Z","bundle_sha256":"84dd628dba892c2c31315b0d7d6d494b9876e23df9c0db21c98b351e5671e04f"}}