{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JETDXABBTKLP5XN3K4OPX6KXTM","short_pith_number":"pith:JETDXABB","schema_version":"1.0","canonical_sha256":"49263b80219a96feddbb571cfbf9579b0efa88a992756a7e02a07e9457b6532d","source":{"kind":"arxiv","id":"2301.01202","version":1},"attestation_state":"computed","paper":{"title":"DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Fang Chen, Feixiang Zhou, Heiko Balzter, Huiyu Zhou, Peng Ren","submitted_at":"2022-12-19T18:23:50Z","abstract_excerpt":"Successful implementation of oil spill segmentation in Synthetic Aperture Radar (SAR) images is vital for marine environmental protection. In this paper, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images. Specifically, our proposed segmentation network is constructed with two deep neural modules running in an interactive manner, where one is the inference module to achieve latent feature variable inference from SAR images, and the other is the generative module to pro"},"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":"2301.01202","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-19T18:23:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"01bdcaf1fa567a8ae317a68da25f903d71dc73669beed8eadc5cd4c2060d471a","abstract_canon_sha256":"f3518113d8dd95cf8feb0167cb1d39070657c66e18ad98b6f6ed56eb35bb852a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:06:09.172843Z","signature_b64":"Bt9OT3LaHPcXcFxsk7yCghTdHz8kSG4t2giPur5QKTWJ6zW2zE/AbF+Xvm235vbsbwqy6bbbV0/VNyc7NwPaDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49263b80219a96feddbb571cfbf9579b0efa88a992756a7e02a07e9457b6532d","last_reissued_at":"2026-07-05T06:06:09.172464Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:06:09.172464Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Fang Chen, Feixiang Zhou, Heiko Balzter, Huiyu Zhou, Peng Ren","submitted_at":"2022-12-19T18:23:50Z","abstract_excerpt":"Successful implementation of oil spill segmentation in Synthetic Aperture Radar (SAR) images is vital for marine environmental protection. In this paper, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images. Specifically, our proposed segmentation network is constructed with two deep neural modules running in an interactive manner, where one is the inference module to achieve latent feature variable inference from SAR images, and the other is the generative module to pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.01202","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/2301.01202/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":"2301.01202","created_at":"2026-07-05T06:06:09.172521+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.01202v1","created_at":"2026-07-05T06:06:09.172521+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.01202","created_at":"2026-07-05T06:06:09.172521+00:00"},{"alias_kind":"pith_short_12","alias_value":"JETDXABBTKLP","created_at":"2026-07-05T06:06:09.172521+00:00"},{"alias_kind":"pith_short_16","alias_value":"JETDXABBTKLP5XN3","created_at":"2026-07-05T06:06:09.172521+00:00"},{"alias_kind":"pith_short_8","alias_value":"JETDXABB","created_at":"2026-07-05T06:06:09.172521+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/JETDXABBTKLP5XN3K4OPX6KXTM","json":"https://pith.science/pith/JETDXABBTKLP5XN3K4OPX6KXTM.json","graph_json":"https://pith.science/api/pith-number/JETDXABBTKLP5XN3K4OPX6KXTM/graph.json","events_json":"https://pith.science/api/pith-number/JETDXABBTKLP5XN3K4OPX6KXTM/events.json","paper":"https://pith.science/paper/JETDXABB"},"agent_actions":{"view_html":"https://pith.science/pith/JETDXABBTKLP5XN3K4OPX6KXTM","download_json":"https://pith.science/pith/JETDXABBTKLP5XN3K4OPX6KXTM.json","view_paper":"https://pith.science/paper/JETDXABB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.01202&json=true","fetch_graph":"https://pith.science/api/pith-number/JETDXABBTKLP5XN3K4OPX6KXTM/graph.json","fetch_events":"https://pith.science/api/pith-number/JETDXABBTKLP5XN3K4OPX6KXTM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JETDXABBTKLP5XN3K4OPX6KXTM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JETDXABBTKLP5XN3K4OPX6KXTM/action/storage_attestation","attest_author":"https://pith.science/pith/JETDXABBTKLP5XN3K4OPX6KXTM/action/author_attestation","sign_citation":"https://pith.science/pith/JETDXABBTKLP5XN3K4OPX6KXTM/action/citation_signature","submit_replication":"https://pith.science/pith/JETDXABBTKLP5XN3K4OPX6KXTM/action/replication_record"}},"created_at":"2026-07-05T06:06:09.172521+00:00","updated_at":"2026-07-05T06:06:09.172521+00:00"}