{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:RHYAVMXNTHQ3JLUH7PKYPXPJ2O","short_pith_number":"pith:RHYAVMXN","canonical_record":{"source":{"id":"1801.08513","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-25T18:22:22Z","cross_cats_sorted":[],"title_canon_sha256":"d044199ef1c9525f503b2d72477d018f5de70ae6d8321c73c82094c0c2ece491","abstract_canon_sha256":"36a3b5cd6bdd58b8bf937f32e4b967a9bb2922f34d8628e127997ca35506d27f"},"schema_version":"1.0"},"canonical_sha256":"89f00ab2ed99e1b4ae87fbd587dde9d3a630dd2afd7fb300650ca4c126494328","source":{"kind":"arxiv","id":"1801.08513","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.08513","created_at":"2026-05-18T00:25:06Z"},{"alias_kind":"arxiv_version","alias_value":"1801.08513v1","created_at":"2026-05-18T00:25:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.08513","created_at":"2026-05-18T00:25:06Z"},{"alias_kind":"pith_short_12","alias_value":"RHYAVMXNTHQ3","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RHYAVMXNTHQ3JLUH","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RHYAVMXN","created_at":"2026-05-18T12:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:RHYAVMXNTHQ3JLUH7PKYPXPJ2O","target":"record","payload":{"canonical_record":{"source":{"id":"1801.08513","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-25T18:22:22Z","cross_cats_sorted":[],"title_canon_sha256":"d044199ef1c9525f503b2d72477d018f5de70ae6d8321c73c82094c0c2ece491","abstract_canon_sha256":"36a3b5cd6bdd58b8bf937f32e4b967a9bb2922f34d8628e127997ca35506d27f"},"schema_version":"1.0"},"canonical_sha256":"89f00ab2ed99e1b4ae87fbd587dde9d3a630dd2afd7fb300650ca4c126494328","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:06.157358Z","signature_b64":"TaJe+ZPeq3TSjfvmEkV8ibk5ARHW2KajrVignoycoMkm/6h6WgN06SK3ZuxdWe4lKCNWLkIJtVmk0rFS9se2Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89f00ab2ed99e1b4ae87fbd587dde9d3a630dd2afd7fb300650ca4c126494328","last_reissued_at":"2026-05-18T00:25:06.156912Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:06.156912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.08513","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":"2026-05-18T00:25:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V6FGZVAh8PsgDLNb5rZrOBp1avzXV9DrjgWVTM7sQiMXpMgT1cm3yMtGz5xNilmg4XbQR7aiSTIjUwEtDY57Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T22:15:34.592408Z"},"content_sha256":"afee8f30cc3538c976dd7271ab1629d0afccb01413a342933d8cd1ae1becedbf","schema_version":"1.0","event_id":"sha256:afee8f30cc3538c976dd7271ab1629d0afccb01413a342933d8cd1ae1becedbf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:RHYAVMXNTHQ3JLUH7PKYPXPJ2O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unmixing urban hyperspectral imagery with a Gaussian mixture model on endmember variability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Erin B. Wetherley, Paul D. Gader, Yuan Zhou","submitted_at":"2018-01-25T18:22:22Z","abstract_excerpt":"In this paper, we model a pixel as a linear combination of endmembers sampled from probability distributions of Gaussian mixture models (GMM). The parameters of the GMM distributions are estimated using spectral libraries. Abundances are estimated based on the distribution parameters. The advantage of this algorithm is that the model size grows very slowly as a function of the library size. To validate this method, we used data collected by the AVIRIS sensor over the Santa Barbara region: two 16 m spatial resolution and two 4 m spatial resolution images. 64 validated regions of interest (ROI) "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.08513","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":"2026-05-18T00:25:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AGWydUSDKfzbPIBsDiMVndrIFOGuND0jxIUpOGTD8IJzL7WFw9l6J06bdm+/zSWeLguW8krrS/uKHDkl2FpNBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T22:15:34.593127Z"},"content_sha256":"354abd7e44829ab5daba8519269a61a9da6a2f8dc17301576d4db9a99cf105cd","schema_version":"1.0","event_id":"sha256:354abd7e44829ab5daba8519269a61a9da6a2f8dc17301576d4db9a99cf105cd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RHYAVMXNTHQ3JLUH7PKYPXPJ2O/bundle.json","state_url":"https://pith.science/pith/RHYAVMXNTHQ3JLUH7PKYPXPJ2O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RHYAVMXNTHQ3JLUH7PKYPXPJ2O/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":"2026-06-06T22:15:34Z","links":{"resolver":"https://pith.science/pith/RHYAVMXNTHQ3JLUH7PKYPXPJ2O","bundle":"https://pith.science/pith/RHYAVMXNTHQ3JLUH7PKYPXPJ2O/bundle.json","state":"https://pith.science/pith/RHYAVMXNTHQ3JLUH7PKYPXPJ2O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RHYAVMXNTHQ3JLUH7PKYPXPJ2O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:RHYAVMXNTHQ3JLUH7PKYPXPJ2O","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":"36a3b5cd6bdd58b8bf937f32e4b967a9bb2922f34d8628e127997ca35506d27f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-25T18:22:22Z","title_canon_sha256":"d044199ef1c9525f503b2d72477d018f5de70ae6d8321c73c82094c0c2ece491"},"schema_version":"1.0","source":{"id":"1801.08513","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.08513","created_at":"2026-05-18T00:25:06Z"},{"alias_kind":"arxiv_version","alias_value":"1801.08513v1","created_at":"2026-05-18T00:25:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.08513","created_at":"2026-05-18T00:25:06Z"},{"alias_kind":"pith_short_12","alias_value":"RHYAVMXNTHQ3","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RHYAVMXNTHQ3JLUH","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RHYAVMXN","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:354abd7e44829ab5daba8519269a61a9da6a2f8dc17301576d4db9a99cf105cd","target":"graph","created_at":"2026-05-18T00:25:06Z","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":"In this paper, we model a pixel as a linear combination of endmembers sampled from probability distributions of Gaussian mixture models (GMM). The parameters of the GMM distributions are estimated using spectral libraries. Abundances are estimated based on the distribution parameters. The advantage of this algorithm is that the model size grows very slowly as a function of the library size. To validate this method, we used data collected by the AVIRIS sensor over the Santa Barbara region: two 16 m spatial resolution and two 4 m spatial resolution images. 64 validated regions of interest (ROI) ","authors_text":"Erin B. Wetherley, Paul D. Gader, Yuan Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-25T18:22:22Z","title":"Unmixing urban hyperspectral imagery with a Gaussian mixture model on endmember variability"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.08513","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:afee8f30cc3538c976dd7271ab1629d0afccb01413a342933d8cd1ae1becedbf","target":"record","created_at":"2026-05-18T00:25:06Z","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":"36a3b5cd6bdd58b8bf937f32e4b967a9bb2922f34d8628e127997ca35506d27f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-25T18:22:22Z","title_canon_sha256":"d044199ef1c9525f503b2d72477d018f5de70ae6d8321c73c82094c0c2ece491"},"schema_version":"1.0","source":{"id":"1801.08513","kind":"arxiv","version":1}},"canonical_sha256":"89f00ab2ed99e1b4ae87fbd587dde9d3a630dd2afd7fb300650ca4c126494328","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"89f00ab2ed99e1b4ae87fbd587dde9d3a630dd2afd7fb300650ca4c126494328","first_computed_at":"2026-05-18T00:25:06.156912Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:25:06.156912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TaJe+ZPeq3TSjfvmEkV8ibk5ARHW2KajrVignoycoMkm/6h6WgN06SK3ZuxdWe4lKCNWLkIJtVmk0rFS9se2Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:25:06.157358Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.08513","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:afee8f30cc3538c976dd7271ab1629d0afccb01413a342933d8cd1ae1becedbf","sha256:354abd7e44829ab5daba8519269a61a9da6a2f8dc17301576d4db9a99cf105cd"],"state_sha256":"b190aedf87d0791724d7e4c3cb204141c50af34df16d94a072333e53cf409d23"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LWkHWULWgaRSsAcR6nemXbnEk0vmAOZuxGbGKu94GeDN/gAD+ANzS7MR3Lw/b7A0PGLw/EjoHZok5P32VvjPCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T22:15:34.596970Z","bundle_sha256":"d89ec131cc3dc8bf96d3bc9369ee0852f5fae93d66f9c62013bf2ba00f1ed9e3"}}