{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:7XAAKTI45P4YVB3MSSX2ZPRCCH","short_pith_number":"pith:7XAAKTI4","canonical_record":{"source":{"id":"1308.1187","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-08-06T05:57:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8fdf922412ea61dd2e08e72692678cc237972d567d84e20825554c5a6096010e","abstract_canon_sha256":"63a924ffa3cc2419935c5ba226d6053d292cd28a403dd05aa39b22a7fdea8926"},"schema_version":"1.0"},"canonical_sha256":"fdc0054d1cebf98a876c94afacbe2211dacdfb1d7ebff22ea35bee3a06487a19","source":{"kind":"arxiv","id":"1308.1187","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1308.1187","created_at":"2026-05-18T03:16:39Z"},{"alias_kind":"arxiv_version","alias_value":"1308.1187v1","created_at":"2026-05-18T03:16:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1308.1187","created_at":"2026-05-18T03:16:39Z"},{"alias_kind":"pith_short_12","alias_value":"7XAAKTI45P4Y","created_at":"2026-05-18T12:27:38Z"},{"alias_kind":"pith_short_16","alias_value":"7XAAKTI45P4YVB3M","created_at":"2026-05-18T12:27:38Z"},{"alias_kind":"pith_short_8","alias_value":"7XAAKTI4","created_at":"2026-05-18T12:27:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:7XAAKTI45P4YVB3MSSX2ZPRCCH","target":"record","payload":{"canonical_record":{"source":{"id":"1308.1187","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-08-06T05:57:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8fdf922412ea61dd2e08e72692678cc237972d567d84e20825554c5a6096010e","abstract_canon_sha256":"63a924ffa3cc2419935c5ba226d6053d292cd28a403dd05aa39b22a7fdea8926"},"schema_version":"1.0"},"canonical_sha256":"fdc0054d1cebf98a876c94afacbe2211dacdfb1d7ebff22ea35bee3a06487a19","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:16:39.569613Z","signature_b64":"eYPsJ3j9UzLykQzGyiI1so2QM2ur9eEqKVpG1DtFFoJhyhXy7dKu4bxux2Hel44FUQGP1bqA5qjFLzzenUfFCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fdc0054d1cebf98a876c94afacbe2211dacdfb1d7ebff22ea35bee3a06487a19","last_reissued_at":"2026-05-18T03:16:39.568915Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:16:39.568915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1308.1187","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":"2026-05-18T03:16:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bZhjqc3U2+hfkwKXzqKxPboB8ZqU6Tzn6uWs4007ahT0zaLYMlm7/8bC7FOdSwz44lsMWkObkBnFBPOujK82Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T15:59:00.309748Z"},"content_sha256":"db8fcbdab127a70fc8458c445b0ad6445d42cc16d2428c8e2af8d827f711229e","schema_version":"1.0","event_id":"sha256:db8fcbdab127a70fc8458c445b0ad6445d42cc16d2428c8e2af8d827f711229e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:7XAAKTI45P4YVB3MSSX2ZPRCCH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spatial-Aware Dictionary Learning for Hyperspectral Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ali Soltani-Farani, Hamid R. Rabiee, Seyyed Abbas Hosseini","submitted_at":"2013-08-06T05:57:08Z","abstract_excerpt":"This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of a spectral sample, with the goal of hyperspectral image classification. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model each pixel with a linear combination of a few dictionary elements learned from the data. Since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1308.1187","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:16:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jUV7fdlzui2QotnvDlqrP6smnan5Y+vuqWKwdsUimLzSAPZ21rNI3yN6SxTO38F+Nuzc2/CPRWySe3C6Rxr8AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T15:59:00.310099Z"},"content_sha256":"3799b6f517a60557673740f93c310841b8ca762ab96580e012af00701dacf3ec","schema_version":"1.0","event_id":"sha256:3799b6f517a60557673740f93c310841b8ca762ab96580e012af00701dacf3ec"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7XAAKTI45P4YVB3MSSX2ZPRCCH/bundle.json","state_url":"https://pith.science/pith/7XAAKTI45P4YVB3MSSX2ZPRCCH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7XAAKTI45P4YVB3MSSX2ZPRCCH/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":"2026-06-09T15:59:00Z","links":{"resolver":"https://pith.science/pith/7XAAKTI45P4YVB3MSSX2ZPRCCH","bundle":"https://pith.science/pith/7XAAKTI45P4YVB3MSSX2ZPRCCH/bundle.json","state":"https://pith.science/pith/7XAAKTI45P4YVB3MSSX2ZPRCCH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7XAAKTI45P4YVB3MSSX2ZPRCCH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:7XAAKTI45P4YVB3MSSX2ZPRCCH","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":"63a924ffa3cc2419935c5ba226d6053d292cd28a403dd05aa39b22a7fdea8926","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-08-06T05:57:08Z","title_canon_sha256":"8fdf922412ea61dd2e08e72692678cc237972d567d84e20825554c5a6096010e"},"schema_version":"1.0","source":{"id":"1308.1187","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1308.1187","created_at":"2026-05-18T03:16:39Z"},{"alias_kind":"arxiv_version","alias_value":"1308.1187v1","created_at":"2026-05-18T03:16:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1308.1187","created_at":"2026-05-18T03:16:39Z"},{"alias_kind":"pith_short_12","alias_value":"7XAAKTI45P4Y","created_at":"2026-05-18T12:27:38Z"},{"alias_kind":"pith_short_16","alias_value":"7XAAKTI45P4YVB3M","created_at":"2026-05-18T12:27:38Z"},{"alias_kind":"pith_short_8","alias_value":"7XAAKTI4","created_at":"2026-05-18T12:27:38Z"}],"graph_snapshots":[{"event_id":"sha256:3799b6f517a60557673740f93c310841b8ca762ab96580e012af00701dacf3ec","target":"graph","created_at":"2026-05-18T03:16:39Z","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"},"paper":{"abstract_excerpt":"This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of a spectral sample, with the goal of hyperspectral image classification. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model each pixel with a linear combination of a few dictionary elements learned from the data. Since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the di","authors_text":"Ali Soltani-Farani, Hamid R. Rabiee, Seyyed Abbas Hosseini","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-08-06T05:57:08Z","title":"Spatial-Aware Dictionary Learning for Hyperspectral Image Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1308.1187","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:db8fcbdab127a70fc8458c445b0ad6445d42cc16d2428c8e2af8d827f711229e","target":"record","created_at":"2026-05-18T03:16:39Z","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":"63a924ffa3cc2419935c5ba226d6053d292cd28a403dd05aa39b22a7fdea8926","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-08-06T05:57:08Z","title_canon_sha256":"8fdf922412ea61dd2e08e72692678cc237972d567d84e20825554c5a6096010e"},"schema_version":"1.0","source":{"id":"1308.1187","kind":"arxiv","version":1}},"canonical_sha256":"fdc0054d1cebf98a876c94afacbe2211dacdfb1d7ebff22ea35bee3a06487a19","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fdc0054d1cebf98a876c94afacbe2211dacdfb1d7ebff22ea35bee3a06487a19","first_computed_at":"2026-05-18T03:16:39.568915Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:16:39.568915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eYPsJ3j9UzLykQzGyiI1so2QM2ur9eEqKVpG1DtFFoJhyhXy7dKu4bxux2Hel44FUQGP1bqA5qjFLzzenUfFCg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:16:39.569613Z","signed_message":"canonical_sha256_bytes"},"source_id":"1308.1187","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db8fcbdab127a70fc8458c445b0ad6445d42cc16d2428c8e2af8d827f711229e","sha256:3799b6f517a60557673740f93c310841b8ca762ab96580e012af00701dacf3ec"],"state_sha256":"9d4b4b79fe4b4e436a63e38e26e76c4237eae4878805810636733771ab560fc9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jX4eTf+ffwFHdtnvlO7oPDq9HcsygIk5fCSklzttgfqmvk2Co/Y7odOJhd1aLD54/3NC8U2gMltK7kf7ZriLCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T15:59:00.312134Z","bundle_sha256":"737012e7fe9bf3b7a173e7c5b12072c7ee28b446a56f7476ea9f5f3a23320091"}}