{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EKRYKFB4QIRRTFFCVKGPROM6TS","short_pith_number":"pith:EKRYKFB4","schema_version":"1.0","canonical_sha256":"22a385143c82231994a2aa8cf8b99e9ca285a1ef6ac818e08b1e59b692d1da34","source":{"kind":"arxiv","id":"2305.12410","version":1},"attestation_state":"computed","paper":{"title":"DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanchun Wang, Huiyu Zhou, Licheng Jiao, Shunli Tian, Xiangrong Zhang","submitted_at":"2023-05-21T09:21:41Z","abstract_excerpt":"Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing due to its ability to detect subtle changes on the earth's surface. Recently, diffusional denoising probabilistic models (DDPM) have demonstrated remarkable performance in the generative domain. Apart from their image generation capability, the denoising process in diffusion models can comprehensively account for the semantic correlation of spectral-spatial features in HSI, resulting in the retrieval of semantically relevant features in the original image. In this work, we extend the diffusio"},"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":"2305.12410","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-21T09:21:41Z","cross_cats_sorted":[],"title_canon_sha256":"06c0d92bce50519c02017634432a3c8e187b627807308f0be5a3f69677970e63","abstract_canon_sha256":"37e6322f724be8b531cfce9ad345c0134321150a78b4c3c1b32b7d7ecd6eaf6a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:12:10.213918Z","signature_b64":"eZX5JIYgKbyDdU6NXldX43L6mgvdappryLplY1QXC61po+G3wLinJnJXXjyslhSJd5avubagD+UcPKNf3Bf/CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22a385143c82231994a2aa8cf8b99e9ca285a1ef6ac818e08b1e59b692d1da34","last_reissued_at":"2026-07-05T06:12:10.213517Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:12:10.213517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanchun Wang, Huiyu Zhou, Licheng Jiao, Shunli Tian, Xiangrong Zhang","submitted_at":"2023-05-21T09:21:41Z","abstract_excerpt":"Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing due to its ability to detect subtle changes on the earth's surface. Recently, diffusional denoising probabilistic models (DDPM) have demonstrated remarkable performance in the generative domain. Apart from their image generation capability, the denoising process in diffusion models can comprehensively account for the semantic correlation of spectral-spatial features in HSI, resulting in the retrieval of semantically relevant features in the original image. In this work, we extend the diffusio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12410","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/2305.12410/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2305.12410","created_at":"2026-07-05T06:12:10.213573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.12410v1","created_at":"2026-07-05T06:12:10.213573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12410","created_at":"2026-07-05T06:12:10.213573+00:00"},{"alias_kind":"pith_short_12","alias_value":"EKRYKFB4QIRR","created_at":"2026-07-05T06:12:10.213573+00:00"},{"alias_kind":"pith_short_16","alias_value":"EKRYKFB4QIRRTFFC","created_at":"2026-07-05T06:12:10.213573+00:00"},{"alias_kind":"pith_short_8","alias_value":"EKRYKFB4","created_at":"2026-07-05T06:12:10.213573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.31029","citing_title":"TerraDiT-$\\Omega$: Unified Spatial Control for Satellite Image Synthesis with Any Geospatial Primitive","ref_index":69,"is_internal_anchor":false},{"citing_arxiv_id":"2509.23310","citing_title":"Balanced Diffusion-Guided Fusion for Multimodal Remote Sensing Classification","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2603.03239","citing_title":"COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Data","ref_index":65,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14341","citing_title":"AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors","ref_index":130,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EKRYKFB4QIRRTFFCVKGPROM6TS","json":"https://pith.science/pith/EKRYKFB4QIRRTFFCVKGPROM6TS.json","graph_json":"https://pith.science/api/pith-number/EKRYKFB4QIRRTFFCVKGPROM6TS/graph.json","events_json":"https://pith.science/api/pith-number/EKRYKFB4QIRRTFFCVKGPROM6TS/events.json","paper":"https://pith.science/paper/EKRYKFB4"},"agent_actions":{"view_html":"https://pith.science/pith/EKRYKFB4QIRRTFFCVKGPROM6TS","download_json":"https://pith.science/pith/EKRYKFB4QIRRTFFCVKGPROM6TS.json","view_paper":"https://pith.science/paper/EKRYKFB4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.12410&json=true","fetch_graph":"https://pith.science/api/pith-number/EKRYKFB4QIRRTFFCVKGPROM6TS/graph.json","fetch_events":"https://pith.science/api/pith-number/EKRYKFB4QIRRTFFCVKGPROM6TS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EKRYKFB4QIRRTFFCVKGPROM6TS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EKRYKFB4QIRRTFFCVKGPROM6TS/action/storage_attestation","attest_author":"https://pith.science/pith/EKRYKFB4QIRRTFFCVKGPROM6TS/action/author_attestation","sign_citation":"https://pith.science/pith/EKRYKFB4QIRRTFFCVKGPROM6TS/action/citation_signature","submit_replication":"https://pith.science/pith/EKRYKFB4QIRRTFFCVKGPROM6TS/action/replication_record"}},"created_at":"2026-07-05T06:12:10.213573+00:00","updated_at":"2026-07-05T06:12:10.213573+00:00"}